DEV Community: Hari Narayan G

Talk to your data – what do we mean by that?

Hari Narayan G — Wed, 07 Dec 2022 09:44:21 +0000

We have all heard it said that one picture is worth thousands of words. Yet, if this statement is true, why does it have to be a saying? — Walter Ong, Orality and Literacy

Imagine you are staring at a dashboard like this. Like many of us who actively use data and insights for our operational decision making.

While interpreting the different numbers and visuals in the dashboard, what you are internally trying to do is to get answers to questions you have in your mind at this point in time.

‘What is the positive cases count as of today?’

‘How has it been trending this year?’

‘Why is it spiking again this month?’

‘How much is it in my state?’

‘How about my city?’

‘How is my city compared to the others?’

‘Which state is worst affected?’

‘Which state is the least affected?’

Essentially, consuming data insights, or in general becoming knowledgeable, consists of two high level parts. The Questions and the Answers.

Questions

In the above example, if these are the standard set of questions that you would consistently be asking every time, then a dashboard like that ideally serves the purpose.

The challenge is that, more often than not, we run into these challenges with any data analytics output.

Our questions are not finite, especially if you are a seeker
The questions varies based on who is asking the question and their knowledge levels
And, most importantly, the answer always leads to more questions

Answers

Some of the answers are direct facts, where you clearly knew what to ask, while some of the answers are revelations, where you did not have the question on your mind, but you were intrigued by the fact. ‘Hmm. I din’t know that’.

Any BI or Data Analytics application should help the users in two primary outcome.

Get objective answers to the straight questions you have in your mind that can be used for decision making or to take actions or simply called as Needs
An equally effective outcome is when it helps unravel answers that you dint’ even know existed. What we call as the wow moments or the ‘Hmm. That’s interesting moments.’

The Conversational approach

Imagine, in the absence of this dashboard, you are talking to someone knowledgable on this topic. A teacher or an expert or a data analyst. How would that interaction be, especially if this topic is closer to your heart and you are in dire need of this information.

Nothing but a series of questions, answers, discussions.

‘What bad is Covid as of today?’

‘How is it trending so far this year?’

‘I see a spike last month. Can you zoom in on that?’

‘How much is it in my state?’

‘How about my city?’

‘How is my city compared to the others?’

‘Which state is worst affected?’

‘How is the vaccination rate in that state?’

‘How is the trend in the states with high vaccination rates?’

All kinds of questions on the topic — one leading to the other, especially triggered by the answer you got in your previous question. In the end, you find that conversation to be quite fulfilling, enriching and most importantly enlightening. That’s the true power and out come of Data and Information.

So, what’s the difference?

You are not constrained by what’s available in the dashboard and
The questions are more aligned to your knowledge level and your sequence of thought, unraveling one answer after the other, eventually leading you to the end goal of being well-informed.
That’s what we mean by ‘Talk to your Data’.

Originally published: Talk to your data — what do we mean by that?

What is Conversational Insights in Under 5 Minutes

Hari Narayan G — Wed, 07 Dec 2022 09:12:11 +0000

What is Conversational Insights?

Conversational insights is a novel approach to analyzing data that uses the natural language of customers, employees, and partners to understand their needs. It allows for better communication, improved insight, and faster decision-making.

Conversational insights is a new way to interact with your business data. It’s more natural and intuitive for users, who can get answers without the added complexities of a query-driven data analytics tool. And it can be used in many industries — from healthcare to manufacturing — to improve productivity and better understand customer needs.

The concept of conversation-driven analytics has been around for some time, but it’s just now starting to gain traction because of its potential as part of the trend toward Natural Language Processing (NLP). This technology is also part of the growing interest in artificial intelligence (AI), which uses computers’ ability to learn from experience or observation rather than being told what to do by programmers or humans telling them how things should work.

Why Do We Need Conversational Insights?

To understand the need for conversational-driven business intelligence platforms, one needs to look at the current suite of self-service analytics tools. They started with the noble intention of enabling everyone to derive contextual stories from data, but have metamorphosed into a form that’s undesirable at large. There are three major shortcomings of the current suite of self-service analytics platforms.

Complexity in Usage: These tools demand a certain degree of expertise that requires training, certifications, and more to use. The difficulty of operating these tools exponentially increases with the amount of data being collected and processed.
Additional Overheads: Specialized teams are employed to create reports when the volume and the level of sophistication surpass the expertise of regular IT teams. This adds to the overheads along with licensing costs.
Time Loss: Even for a seasoned user to create dashboards and reports, will take him or her a specific amount of time. The time loss is directly proportional to the volume of reports.

The impact of shortcomings affects businesses heavily, often resulting in loss of revenue.

Information Overload: An excess of information to make a data-driven decision leads to employee burnout, and failing productivity levels.
Painful Delays in Data Access: Time loss in delivering dashboards coupled with information overload hits the business where it hurts. Taking data-driven time-bound decisions. Hence it’s imperative to implement a different business intelligence system, one that’s intuitive to how humans access information.

Are there any Benefits in Implementing Conversational Insights?

For decades, the adoption of business intelligence tools has hovered in the range of 20–30% of users in an organization. Business Intelligence systems were used only by a few within the organization and not tapping their full potential. Conversational Insights is designed to improve adoption amongst all data users by encouraging them to access insights in the language they speak.

Introducing intuitive business intelligence platforms to the middle and senior management team or whoever is part of the decision-making, will lead to a manifold increase in the company’s revenue. AI-powered conversational insights enable business users to find information on the go. Ad hoc queries can be resolved quickly by BI teams, taking only a few seconds as opposed to days or weeks. What’s more important is that the system will be able to learn and improve continuously.

Enhanced Returns: Enables business users with actionable insights and allows them to uncover business issues even before they occur

Higher user adoption: A straightforward language-based interface that enables even all users in the organization to use the tools with basic training

Data democratization: Access and understand data without analytical, statistical, or data-handling skills

Improved decision-making: A search-driven analytics platform allows users to dive deeper, discover AI/ML-powered insights, and find the most granular information by allowing them to explore data in any direction

The Future of Business Intelligence will be Conversational

Conversational insights is the future of business intelligence and is here to get the most out of available data and make better decisions. Voice-enabled data analytics help HR managers find the right people, engage with them, and build a relationship before they even decide to hire them. This approach enables sales managers to understand customer emotions and build tailored experiences for them. Supply chain personnel can plan to mitigate the risk of dwindling SKUs and proactively plan effective shipping routes. The applications of a conversational insight tool are endless.

Intrigued to learn more about conversational insights? Check out our webinar where we discuss the story of how conversational insights is revolutionizing the data analytics industry.

Originally posted: What is Conversational Insights in Under 5 Minutes

Top 6 Data Literacy Tips for Beginners

Hari Narayan G — Wed, 07 Dec 2022 07:17:33 +0000

More than ever, corporations across all business sectors are gathering essential data. This is because intelligent data helps businesses achieve higher profits, enhance productivity, streamline processes, and improve decision-making. Corporations acknowledge the benefits and need for overall data understanding. However, strengthening the skills of an entire workforce is never easy.

Data literacy refers to one’s ability to interrogate, analyze, work with, and read data. The importance of data literacy is widespread as not only IT but other department heads also understand the need to make data-driven decisions. A major drawback of not possessing data literacy is painful delays in accessing data and deriving insights. The lower the data literacy capabilities of the team, more the time is required to access relevant information.

Then it should come as a no-brainer when IDC told the world that an average knowledge worker spends 30% of their working hours searching for relevant information.

Precious business hours that can translate to more value-added work are being spent foraging for information. Hence, it’s imperative to train and enhance the data literacy skills of resources inside the organization. Wondering how you can effectively educate the employees? Continue reading, and you will get meaningful insights on how to go about it.

Tips to Enhance Your Workforce’s Data Skills

Data literacy is not a skill that your employees can learn overnight. It incorporates problem-solving capabilities, and basic technological skills, along with analytical and critical thinking. Developing each aspect requires significant experience, training, and time.

Upskilling your workforce is the most effective option that can help boost data skills. Here are some tips you can employ in your organization:

1) Develop a data literacy training program

The changing nature of jobs, technological advancements, and uncertainties in the business environment has resulted in skill gaps. A data literacy education program will help you handle the existing skill gaps in your organization. But how can you develop an efficient program?

There are different data literacy certification courses available online to start out. They will help define data literacy competencies and design learning experiences to suit the organizational needs.

2) Ensure data skills are measurable

The next step must be skill assessment which will portray your employee’s capability levels post the training session. This will deliver oversight of existing competencies to your employees, learning and development managers, and the management.

Continuous assessment along with introducing data competencies as a measurable quotient in the appraisal process will improve implementation. It will also help identify the actual impacts of the learning program.

3) Create a data-driven organizational culture

A great inhibitor to implementing data-driven decision-making is a term called “gut feeling”. This habit is more prevalent among experienced talent inside the organization, who rely on instincts more than data. The data literacy program must also take into consideration this attitude and address its root causes.

The best way is to portray a holistic picture of use cases, tools, and methods of handling data. In that case, you will enhance the employee’s knowledge of data and its impacts. To achieve that, you must ensure the learning program allows employees to understand the basics of various tools and applications. A learner can start out with basic spreadsheet tools like Excel, and Google Sheets. They can advance through the course to reach self-service BI tools like Power BI and Tableau.

4) Introduce a Change Management Strategy

An attitude that’s the biggest example of Newton’s first law in human behavior – Employees are used to executing a set of tasks in a particular way. They tend to stay on that path and when any new method is introduced, they tend to reject it at face value. This is true in implementing data literacy programs and data practices inside a department or an organization.

As explained previously, instinct takes over, and the “we know better” attitude presents itself. To battle this inertia, it’s important to have a change management strategy. One of the points to improve adoption is to introduce data literacy as a key metric in the appraisal cycle explained earlier. The other methods in change management will be elaborated further.

5) Employ a team of SMEs for data practice evaluation

Subject Matter Experts (SMEs) who breathe and live data will be a major player in organizations adopting data literacy practices. SMEs need not necessarily be from the same department; rather pick leaders across various functions whose decisions depend on actionable and data-driven insights. This team of experts will be equipped with valuable insights that will guide your workforce in delivering best practices. Having an SME within a function will ensure that all tasks being executed are aligned with a data-driven mindset. This is another method to implement a change management strategy.

Make sure to employ data analysts, data engineers, and data scientists to support these SMEs. Depending on the data source and operations scope, it is an ideal composition to help in data practice evaluation.

6) Establish a data center of excellence incorporating individuals from all corners

You need to create a team of skilled and knowledgeable individuals who can provide best practices on data skills. Having a data center of excellence not only across functions but within various Shared Service Centers can also improve the overall adoption of data literacy programs. Even small enterprises outsourcing shared services to BPOs or setting up a hybrid ecosystem must stress on this practice.

CoE members will be responsible for monitoring adoption levels and implementing data best practices. They will be at the forefront of driving these changes and help disseminate knowledge across the length and breadth of the organization.

Conclusion

Data is now a valuable resource in the corporate sector and will continue becoming more impactful and essential in the coming years. Data allows every organization member to create a change. It may seem challenging to enhance the data skills of an entire workforce. However, it is a necessary and practical process in the current data-dominated environment.

Too much work to start out and become data-driven? What if we say there’s an easier way? Check out our webinar “Enabling Frontline Managers to Take Data-Driven Decisions with Conversational Insights” and get all your data questions answered today!

Originally posted: Top 6 Data Literacy Tips for Beginners

Data Lake – The Next BIG Thing in the World of Data Storage

Hari Narayan G — Wed, 07 Dec 2022 05:25:01 +0000

Defining a Data Lake

“The Data Lake Market was valued at USD 3.74 billion in 2020 and is expected to reach USD 17.60 billion by 2026, at a CAGR of 29.9% over the forecast period 2021 – 2026. Data lakes have become an economical option for many companies rather than an option for data warehousing.” – Mordor Intelligence

A data lake is like other data storage systems, a repository to collect and store data for further processing. A deeper definition of a data lake is explained below. It is a centralized repository in which enterprise-wide data that can be structured, semi-structured, or unstructured are saved. Data Lake ensures access restrictions pending authorization and improves the ease of data access.

The data can be stored in its native format without the need for structuring it. Various analytics types can be run on it, including big data processing, data visualization, dashboard creations, etc. Data lakes are highly scalable and complex data operations can be performed inside them.

Looking Under the Hood

Data lakes are a way to store, analyze and use vast amounts of data in one place so that it can be analyzed together as one entity — which makes it easier to find patterns within the information being stored. Data lakes also reduce latency between when new information is collected and when it’s analyzed because it doesn’t need to be transferred all over again between systems before being explored by someone who knows how best to use them.

The architecture of a data lake consists of three main zones.

Landing Zone – The landing zone has one major function, which is to bring all the raw data into a single point and then clean it
Staging Zone – The staging zone acts as an area where data transformations happen for data analytics
Exploration Zone – The exploration zone to feed processed data into various analytical tools or to train machine learning models

Learn the Difference – Data Lake vs Data Warehouse

The first thing to note about data lakes is that they are different from traditional data warehouses. A data warehouse is designed to store all of your company’s structured data in one place, which involves a set of preformatting exercises to be performed before loading it into the warehouse. Data lakes on the other hand are designed to store all kinds of structured and unstructured information, so you can use them as a single source for business intelligence (BI).

Data lakes are great because they let you get rid of old silos—data warehouses—and make sure all your BI tools work together seamlessly across various analytical platforms or domain-specific tools like Salesforce or Power BI. The major differences between a data lake and a data warehouse are outlined below.

The Top Benefits that makes a Data Lake Desirable

Data lakes are flexible. They can be used for any type of data, which means you don’t have to worry about whether your data is structured or unstructured. In addition, they’re elastic in nature and can handle large volumes of information with ease; this means that as your business grows, so does your data lake! Data lakes also make it easy for businesses to add new types of data over time without having to worry about how they’ll organize or store their information.

A data lake that is run on the cloud can help a company obtain actionable business insights by permitting the company to use analytics on historical data as well as new data sources. Some examples of these new data sources include log files, clickstreams, social media, and Internet-connected devices. Having a cloud data lake provides a foundation for a company to digitize its business and turn data into a high-value asset.

Optimized Cost: Cloud storage providers offer a variety of storage and pricing options that can help save you money.
Scalability: It provides businesses with the ability to compute and access storage capacity on demand. This functionality is essential for businesses that experience spikes in demand or require extra capacity on a short-term basis.
Single Source of Truth: Data lakes provide a centralized repository for all your data, making it easier to govern and manage access to your data. This allows for greater process efficiency and collaboration among teams.
Security: When it comes to data security, cloud storage providers follow a shared responsibility model to ensure the safety of your information.

Data Lakehouse – Bringing the best of both worlds

Data lakehouses are a new type of system that enables the best of both worlds – data warehouses and data lakes to work together by using similar data structures and management features. This means that data teams can move faster because they only need to access one system. Data lakehouses also make sure that teams have the most complete and up-to-date data available for projects like data science, business intelligence, and machine learning.

Data Lake – A Revelation for Modern Data Storage

The term “lake” refers to a body of water that’s deep and wide enough to hold water, but shallow enough so fish can swim around in it. This analogy works because when you store all of your company’s information in one place (the “data lake”), it becomes easier to manage and analyze. In order to take advantage of this central repository, many organizations have begun investing in data lakes. However, whether it works is left up to the storage and analytical requirements of the organization itself.

Looking for more engaging information regarding data engineering topics? Check out our data glossary page where we try to talk about everything data.

Originally posted: Data Lake - The Next BIG Thing in the World of Data Storage

Exploring the Concept of Data Governance: Definition, Need, and Process

Hari Narayan G — Tue, 06 Dec 2022 09:57:30 +0000

In today’s digital world, organizations need to get smarter about managing their data. They should have a clear idea of what their business is trying to accomplish by using data, what kind of data they have and how data can be used to serve their customers and partners better. This article will help you understand the term data governance, what it is and how it can improve your business practices with regard to your company’s big data.

Defining Data Governance

”Companies must adapt their data governance program to the reality of data explosion and disruptive technologies” — Deloitte

Data governance is the process of managing data for purposes of governance, reliability, quality, and security. It is a combination of business policies, procedures, and practices that govern the collection, storage, transformation, sharing, and usage of data in the organization. It is a way to ensure data security and reliability at the same time. Data governance should be comprehensive, spanning all relevant data types and processes within the organization. However, it is important to note that there are different processes for different types of data. For example, you need to have a data governance process for your data collection in general, but expect that process to change for client’s data, employee data, etc.

Framework to Define Data Governance

A data governance framework establishes the groundwork for data strategy and compliance by providing a data model that describes the data flows, inputs, outputs, and storage parameters, followed by a governance model that outlines the guidelines for handling those data flows, activities, responsibilities, procedures, and processes. The framework is dependent on three critical factors.

Data Scope: This includes the type of data, the nature of the data, and so on. Some common examples would be analytical data, big data, master data, etc.
Defining Roles: Understanding the different roles and responsibilities defined for the data leaders who will be setting up the data governance process like chief data officer, business head, internal IT team, and more.
Data Standards: Defining the standards and guidelines that need to be followed for data capture, processing, management, and other data processes to have a templatized approach.

Framing the Process of Data Governance

Properly integrating data governance into an organization’s data creation, data management, and protection processes, can help businesses amidst the ongoing technology disruption. As Deloitte calls it, “Developing an effective data governance program for the next-generation platforms staged to manage this landscape (of disruptive technologies and data explosion) is essential to harness the data’s potential and to help minimize risks.”

A robust process is a major player in setting up good data governance practices and they are broken into two major parts.

Documentation and data process definition

In addition to being an audit requirement, documentation should clearly delineate all processes. Furthermore, procedures should be reinforced through training and motivational incentives. Make sure the following steps are involved while implementing any new data-related practices.

Identify: Understand your business processes and identify the data types you need to collect.
Govern: Establish policies to govern data usage, control access, and audit usage
Purge: Get rid of data that is no longer relevant or used.
Review: Continually evaluate data practices and identify ways for improvement.

Data integrity requirements

There are chances of loss of data integrity during data collection due to several aspects including human error. Automating the process with simple equipment like bar code scanners, and QR code scanners can go a long way in ensuring the authenticity and integrity of the data collected.

Benefits of Data Governance

Data is at the center of all computer and technology functions, including accounting and finance, planning and control, order management, customer service, scheduling, process control, engineering, and design. Every aspect of an organization’s business nowadays relies on data. Without accurate, reliable data, these systems and functions would be ineffective. The benefits of good data governance include but are not limited to:

Better Quality Data: Better quality data will help your business make better decisions
Better Decision-Making: Better decision-making will result in better customer service and will improve your business’s financial performance
Better Security: Better security helps protect your data from cyber threats and hacks.
Better Compliance: Better compliance helps your business meet all the legal and statutory requirements
Better Business Processes: Better business processes help your business function efficiently and effectively

Data Governance Best Practices

Experts agree that five “best practices” for data governance are essential in making the data practice stand out.

Start Simple, Complicate Later

Starting from scratch (and have never had a data governance process in place) is inherently difficult. Starting small is always wise, as it helps you to test out your ideas and grasp of data governance, acquire skills, and validate the approach. It is critical to stay focused on the big picture by documenting the high-level goals of your project (i.e., what the data governance process might look like). At the same time, you should test your approach through a “pilot” test by carving out a modest portion of your project. In other words, test your approach by validating it with a small piece of your project.

Have a Data Governance Team

A data governance team is required for any data strategy cross-enterprise project. The team will encourage the data strategy across the company and advocate for it, communicating it to others. The team will also be accountable for enforcing data strategy requirements, modeling the desired data mindset, and arbitrating data disputes among business units.

Align to a Business Case

The business case should include a high-level description of the project, a statement of the goals and objectives, expected benefits, and a timetable with milestones and indicators of progress and success (indicators). These indicators help keep the project on track as the project team assesses progress against the predetermined timeline and milestones. The business case also reminds team members of the reasons why you are doing this project and why it is critical to the organization to get it done on time and correctly.

Identify and Implement the Right Metrics and KPIs

The lack of metrics is often not an issue, but rather the opposite. Even when automated, metrics take time and labor; someone has to look at the results, interpret them, and possibly take corrective action. Too many metrics — or metrics that are not meaningful — cannot be beneficial. This can also lead to a lack of attention to truly crucial measurements. Similarly, KPIs (key performance indicators) are usually better handled by keeping a manageable number of critical and critical KPIs than by keeping all of them.

Information-Driven Change Management

Change is uncomfortable for most people because they are nervous about the unknown — the best remedy is information. Make your new processes and procedures as transparent as possible by being open with those who will be affected by them. Explain what you are doing and why. Let them know how their work lives will change as a result of the new procedures and procedures. Tell them how important it is for them to cooperate and support the changes. Involve those who will be most impacted in the planning and implementation of the new procedures. They will be in a better position to see how the changes will affect productivity, how they might be modified to be less intrusive, and how the process might be improved to provide better data.

Conclusion

Data is at the core of any business. Yet, most companies struggle with managing and using it. This may be due to the fact that many businesses don’t understand what data is and what it does for them. With the help of data governance, you can ensure that you’re creating a better experience for your users by providing them with better data. You can also use data governance to get a better understanding of your data and find out which data is no longer being used, which data is no longer relevant, which data is out of date, and which data can be replaced with better data. Data governance can help your business in so many ways, and it’s important that you learn more about it so that you can leverage data to its fullest extent.

Interested in knowing more about the concepts of data engineering? Check our data glossary to brush up on your data basics.

Originally posted: Exploring the Concept of Data Governance: Definition, Need, and Process

NLP vs NLU – Understand the Differences

Hari Narayan G — Mon, 05 Dec 2022 10:18:57 +0000

Ever wondered why you can talk to digital assistants like google assistant, Alexa, and Siri? NLP and NLU make it possible for computers to understand human speech.

There is a common misconception among many people: computers cannot understand human speech, which isn’t the case as artificial intelligence can communicate with human beings.

Artificial intelligence has significantly evolved over the years to a point where understanding human language poses no problem. There are two processing methods that are vital when it comes to developing the structure for machines to understand human speech.

The idea of being able to have conversations with machines must be credited to Alan Turing, whose paper laid the foundations of NLP technology.

What is Natural Language Processing?

Natural Language Processing is a divaricate of computer science; precisely, a branch of artificial intelligence, the act of applying various computational procedures or techniques to perform synthesis and analysis involving speech and natural language.

What is Natural Language Understanding (NLU)?

NLU — Natural Language Understanding is an aspect of Artificial Intelligence that focuses on using computer software to make it possible to understand any form of data through text or speech. NLU is what makes it possible for humans to be able to communicate with machines. Machines can understand the different languages humans use, such as Spanish, English, French, Japanese, etc., and comprehend commands given by humans without having to use the version of computer language, the formalized syntax.

What are the Factors Differentiating NLP & NLU?

NLP and NLU share a lot of things but aren’t at all the same. Many ways in the mode they operate, what they focus on, and their coverage differentiate them from one another. Below are some of the critical differences between NLP and NLU:

When discussing NLP and NLU, the most significant difference is that NLU’s area of focus is figuring out the meaning of a sentence. At the same time, NLP emphasizes creating algorithms that can identify and understand the different natural languages
NLU makes it possible to comprehend language, while NLP helps machines break down and process language
Natural Language Understanding is more concerned and revolves around sentiment analysis which is the process of using text to get information to make it possible for machines to figure out the emotional tones of any reader
NLP and NLU receive every data, but NLP is often used instead of NLU when the task is to find patterns in a large data sample that is mainly filled with unstructured data that need to be converted to structured data. At the same time, NLU focuses primarily on changing structured data to unstructured.
NLU has a much broader concept, while NLP has a very narrow concept

What Defines the Need of Both NLP & NLU?

We need both NLP and NLU as they work hand in hand to help resolve many human problems. They don’t contradict each other and are similar, making them able to help each other and coexist, making them an excellent choice, especially for companies who want to use AI.

LinkedIn in 2017 added NLP and NLU to its platform. As a result, people could find the content they were looking for with ease, and it successfully created a very conducive environment for its users to use the platform to its best potential.

When are Machines Intelligent?

Machines are intelligent when they can perform the task given to them properly while not in a reliable or stable environment for doing the work. Intelligent machines monitor activities in their environments, adapt and change its action to correspond with the best response for each situation.

NLP & NLU use cases

NLP and NLU are very versatile and widely used in any machine. Below are some of the most popular ways in which they are used:

1. Message routing and IVR
Interactive Voice Response (IVR) is used for routing calls and self-service. It was mainly about push buttons and didn’t involve any AI during its earlier versions. Still, with its more advanced version being used, NLU and NLP have helped increase its coverage, making it possible for users to communicate with it through voice.

2. Conversational Chatbots
NLU is one of the driving factors and the primary technology behind conversational chatbots. A conversational chatbot is an automated program that has conversations with humans using natural language through voice or text. Chatbots are usually given a script and can’t divert to anything that isn’t related to the script. They have become essential tools for maintaining good 24/7 customer service for companies.

3. Grammar Checker
Grammar checking is one of the most prominent and commonly used Natural Language Learning (NLP) applications. Tolls are used to check grammar, observe, find and correct every grammatical error in the text. As a result, NLP helps people learn a language, write a book, audit, etc.

4. Machine translation
Computers can learn, mature, and adapt due to the AI branch called machine learning. The algorithms used in machine learning make it possible to create text from nothing. With the machine learning algorithm, millions of texts are analyzed by the computer when it comes to translation to learn how to translate text from one natural language to another correctly.

5. Data capture:
This is assembling and taking note of information concerning a device or an object, event, person, etc. E.g., companies that use NLU in e-commerce can make it possible for customers to input their billing or shipping information through speech. The software then interprets what each customer says and translates the data into words, writing it down.

Some other use cases of NLP and NLU are:

Virtual Assistants
Sentiment analysis
Search Auto correct, autocomplete
Analytics
Speech recognition

Conclusion

NLU and NLP have come a long way, and thanks to advancements in technology, they are now used in multiple ways. They are usually used together because that gives the best performance, especially when conversations between two parties are involved. Separating them will only limit the range of activities that you can achieve. NLP works well with any data, but NLU is limited to structured data, meaning that while NLP can have dates or times in its conversations, NLU can’t.

Originally posted: NLP vs NLU — Understand the Differences

What is a Data Warehouse?

Hari Narayan G — Mon, 05 Dec 2022 09:56:21 +0000

Introduction

A data warehouse is one of the key components of any analytics solution. It is used to store and analyze data from various sources, including other databases and transactional systems in a structured format. The information stored in a data warehouse can be accessed by any business user who has appropriate permissions.

Data Warehouse Definition

A data warehouse (DW) is a digital storage system that connects and harmonizes large amounts of data from many different sources. Its purpose is to feed business intelligence (BI), reporting, and analytics, and support regulatory requirements - so companies can turn their data into insight and make smart, data-driven decisions. Data warehouses store current and historical data in one place and act as the single source of truth for an organization. Data warehouses are especially beneficial to organizations because they provide a centralized location for all of an organization's data which can then be used to support various business needs such as BI, reporting, analytics, and compliance. A key point to note here is that data warehouses can handle only structured data.

The Need for Modern Data Warehousing

A data warehouse is one of the key components of any analytics solution. It's a central repository for data from multiple sources and can be used to store historical data and make it available for analysis. A good way to understand this is by thinking about your own life: if you have a car, there are probably many different tools that help keep it in good condition. You might have an air filter, oil change reminders on your calendar (like we did!), and even some instructions on how to change your own oil in case something goes wrong with yours! These all help keep your car in optimal condition so that it lasts longer than expected. However, when someone has trouble understanding exactly what needs fixing or replacing because they don't know what happened last time they took care of their vehicle themselves - or even worse yet haven't been able to figure out why they're having issues again - they may call upon someone else who specializes in working on cars more frequently than most people do; maybe even one who knows more than just basic maintenance procedures."

Data Warehouse vs Database

The difference between a data warehouse and a database is that the former is designed to handle large amounts of data, whereas the latter only handles small quantities. A typical OLAP cube contains billions of rows and hundreds of columns, whereas, in a traditional relational database, you'll find not more than a few million records. A data warehouse can hold many different types of information from various sources: financial reports from various organizations; customer profiles from marketing departments; product sales statistics from multiple departments within your company.

Data Warehouse schema

The data warehouse schema is used to define the data that will be stored in the data warehouse, which is then populated with operational and historical data.

The definition of a data warehouse schema is different from that of an overall database or operational system's (OLTP) model. In fact, it can even be thought of as being far more abstract than either one: while OLTP models are usually defined using relational algebraic languages like SQL or PL/SQL, DW schemas are defined using XML documents only because they're designed to describe things like transactions and business rules without implementing them on top of an existing set (as opposed to relational algebraic modeling).

Data Warehouse Real-Life Use Cases

A data warehouse is a repository of data, which you can use to make business-related decisions. It's a critical tool in the analytics process, but many different ways exist.
Business Intelligence: Data warehouses are used to provide business intelligence (BI) reports that show trends and patterns in your organization's performance. Managers and executives use BI so they can make informed decisions about what actions need to be taken next.

Data Science: Similar to BI, data science uses analytics tools like machine learning algorithms and predictive models in order to predict future outcomes based on past behaviors or events within an organization's current environment at any given time period such as day/month/year, etc

Conclusion

A data warehouse is the central repository of all the data that is captured by your analytics solution. It stores not only relational tables but also any other types of structured or unstructured data (such as text files), which can be queried to extract information from it. It may also contain some indexing capabilities, allowing for fast lookups in any particular column or group of columns in a database table.

Originally published: What is a Data Warehouse?

What is Master Data Management?

Hari Narayan G — Mon, 05 Dec 2022 09:41:50 +0000

Master Data Management (MDM) is the use of tools, processes, and solutions to help an organization manage its master data. It involves the creation of a unified view of all data across an organization and allows for the identification of gaps in data quality.

Defining Master Data Management

Master Data Management is the process of creating and maintaining a single master record — a single source of truth — for each person, place, and thing in a company. Enterprises can use MDM to share key data across the company and produce better reporting, decision-making, and process efficiency.

Master Data Management is used to ensure that a company has accurate and consistent data throughout the organization. It’s also important because it allows businesses to properly manage their internal processes, which can help them increase efficiency and improve customer service.

Understanding the Need for MDM

MDM is a process that ensures that the data used by an organization is accurate and consistent. This makes it easier for organizations to make decisions, especially when it comes to making business decisions based on facts rather than assumptions or beliefs.

Avoid Re-verification

MDM helps companies avoid costly mistakes, such as getting stuck in a cycle of re-verification (re-checking existing records) because they don’t have all the information they need. In fact, studies show that companies with good data management practices are able to save between $65 billion and $200 billion annually in costs associated with poor performance on quality measures alone!

Single Source of Truth

MDM is used to ensure that a company has accurate and consistent data throughout the organization. The goal of MDM is to create a single source of truth for all business data and information, including employees, customers, and partners.

Collaboration

MDM allows for the creation of an environment where multiple users can work on one project at the same time without losing track of what they’re working on or who else may have access to that information. This helps prevent errors from occurring when processing different types of transactions in different formats — for example, account numbers versus credit card numbers versus social security numbers versus passport numbers versus home addresses, etc.

Identifying Different MDM Implementation Models

Master data is the most important data in a business. It consists of people (customers, employees, and suppliers), offices and locations (offices and locations), and goods (products and assets). Even though master data comprises a small percentage of all business data, it is incredibly complex and crucial. MDM implementation models are divided into two categories

Traditional model: In this case, the software is installed on-premise and linked to a single system of record (SoR). The data is stored in a database or an enterprise resource planning (ERP) system that supports MDM requirements.
Hybrid model: In this scenario, the data resides in both an off-premise source and on-premise target systems while only certain information needs to be synchronized between them using agreements or APIs. This type of architecture can be used when you want to centralize your users’ access rights into one location but still allow them access from multiple sources like smartphones and tablets that may not be connected directly via network connectivity at all times.

The Benefits of MDM

Master data management has a lot of other advantages besides helping an organization make good decisions and answer key questions. Here are just a few.

Improved customer experience by providing a consistent, seamless experience for your customers. This can mean improved marketing efforts, a more tailored product offering, and reduced churn rates.
Improved company performance by managing your internal resources better so that they’re working on the right things at the right time with minimal overhead and wasted effort on unimportant tasks.
Increased revenue through increased sales opportunities and cost savings by reducing errors in processes or reporting requirements.
Reduced costs by eliminating redundancies across departments or business units within an organization.
Improved data quality by reducing duplication and improving consistency, which in turn reduces the risk of identifying bad data. In addition, it can also reduce latency between producing a report or emailing an information request. Establishes baselines for each business process, identifying deviations from those baselines, and establishing corrective actions.
Improves data access by providing tools that enable employees at all levels of an organization to see information about their work tasks or projects at any time. For example, if you have an employee who needs to enter an expense report into your accounting system but does not have access because it’s not yet been approved for release into production, MDM will provide them with direct access so they can complete their task without having wait until someone approves it first!
Increases consistency by standardizing how different systems interact with each other so there aren’t any unexpected interactions between systems which could lead people down bad paths when trying something new like using one system instead of another because they don’t fully understand how everything works together yet when combined together correctly (like two datasets), then maybe only one dataset would be needed instead of both datasets being required separately).

Conclusion

Master Data Management is a process that organizations use to ensure that they have accurate and consistent data throughout their organization. MDM helps companies make sure that the data they collect is relevant to their business needs, so they can make informed decisions about their operations. This can be especially important when it comes to developing new products or services, as well as making sure existing ones are working properly so customers aren’t affected by errors in their data.

Looking for more engaging information regarding data engineering topics? Check out our data glossary page where we try to talk about everything data.

Originally posted: What is Master Data Management?

What is Data Modeling?

Hari Narayan G — Mon, 05 Dec 2022 09:28:06 +0000

Introduction

Data modeling is the process of creating a model that describes the structure and relationships in your data. The goal of data modeling is to help you understand how your data relates to each other so that you can use it effectively and efficiently.

What is Data Modeling?

Data modeling is the process of creating a data model to represent real-world situations. This can be done for many reasons, including:

Understanding how your data is structured and how it relates to other things in your system
Designing database systems (including applications) based on this understanding
Creating database schemas

The Need for Data Modeling

Data modeling is a process of understanding data and its relationship with objects. It helps you understand how your business processes work so that you can make better decisions about how to structure your system and improve performance.

Data modeling involves identifying the entities in your system, such as customers, products, or orders; then determining their attributes (e.g., name and address) and relationships (such as an order has a customer). Once these entities are identified, they can be used by other parts of the application to perform tasks like adding new records or updating existing ones automatically when changes occur at all levels within the organization’s hierarchy – from person-to-person interaction down through departments/divisions/branches etcetera – without needing additional code changes being made manually each time something changes.

Types of Data Modeling

There are three main types of data modeling:

Entity Relationship Modeling (ERM) is a method used to define the relationships between entities in your organization’s database. It allows you to create a detailed picture of how your data will be organized, including its structure and relationships. ERMs are often used for large-scale enterprise systems with multiple users and departments, where each department may have its own set of rules governing the way it manages its data.
Dimensional databases are less rigid and more flexible than relational databases. A dimensional database structure is optimized for online queries and data warehousing tools. Critical data elements, like a transaction quantity, for example, are called “facts” and are accompanied by reference information called “dimensions,” be that product ID, unit price, or transaction date. A fact table is a primary table in a dimensional model; retrieval can be quick and efficient – with data for a specific type of activity stored together – but the lack of relationship links can complicate analytical retrieval and use of the data. Since the data structure is tied to the business function that produces and uses the data, combining data produced by dissimilar systems (in a data warehouse) can be problematic
Relational Data Models (RDM) use the same basic principles as relational databases do but require fewer assumptions about how those tables should be structured. This makes them easier for teams to work together on large projects — especially when those projects involve multiple companies or even countries! They also provide better performance in the context of data analytics as opposed to traditional SQL-based database models because they don’t assume any particular object orientation (OO) requirement; they’re just based on flat lists stored in memory instead of trees or graphs stored on disk(s).

Benefits of Data Modeling

Understand the data and its relationship with objects
Design the database
Implement the database, including updating and maintaining it
Solve problems in the database, for example, when you want to merge two tables (e.g., “The number of subscribers has been increased by 10%”)
Delete an item from a table that no longer exists in reality

Conclusion

A well-thought-out and complete data model is necessary for the development of a truly functional, useful, secure, and accurate database. The conceptual model describes the components and functions of the data model and lays out all the components and functions of the data model. The logical data model describes how the data flows through an application, while the physical data model describes how to create tables with columns, rows, and fields that store specific data types.

Interested in knowing more about the concepts of data engineering? Check our data glossary to brush up on your data basics.

Originally posted: What is Data Modeling?

Hello, World – Language as the New Interface

Hari Narayan G — Thu, 01 Dec 2022 10:47:34 +0000

Today, we are only beginning to see the potential of chatbots, virtual personal assistants, and other forms of conversational AI in changing our lives. Well, it seems as though language as an interface will be the future because of conversational AI. In fact, almost anyone can engage in conversation over text at any point in time. But it takes a deep design sense, and seamless interaction capabilities between humans and machines, to switch to language as an interface.

Language intersects through so many levels right from cultural nuances to communicating using voice and text. Who could forget the witty responses from Cortana when it was launched? Siri was ahead of its time during its introduction. Borrowing the words of Laura Kleiney, this was the first time people were able to experience an almost equivalent of HAL or the computer of Captain Kirk in action.

On a personal note, I think this introduction set up an expectation for you, readers. So, without further ado, let’s leap into the story!

Leapfrogging into the Future with Language

Forecasts suggests that by 2024, the number of digital voice assistants will reach 8.4 billion units — a number higher than the world’s population - STATISTA

Adopting digital voice assistants to automate most of the business, as well as personal chores, have been a trend that continues to rise. Especially with the advent of IoT and smart devices, this trend will penetrate deeper into the world of consumerism, and right at the center of voice assistants sit our hero — Conversational AI.

Any Conversational AI platform, be it your regular chatbot or an advanced out-of-world system is built around one premise — Context. If the technology is unable to understand and respond within the context of the conversation, the project has failed. So it’s imperative that the conversational AI system is adequately trained to understand the nuances of normal conversations, to make them seamless. And more importantly, to make it human.

Two things are now very clear.

The Voice assistant usage trend will explode exponentially
Context drives the engine of Conversational AI, the cornerstone of voice bots

This drives us to our most important question — What helps in setting the context? What is that one tool that drives conversations?

Language. It’s safe to presume that language is going to make machines intelligent and as the famous quote goes,

Machine Intelligence is the last invention humanity will ever need to make — Nick Bostrom

We believe that language — a simple tool used by our species since time immemorial will act as the bridge for a tech-driven, sustainable, and intelligent future.

Language Interfaces — Need, Advantages & Disadvantages

Exactly. Things are good as it is. Why does the world need language interfaces when graphical user interfaces are perfectly capable of executing tasks?

The answer is to strive for simplicity. Humans have always placed convenience at the root of all the activities they do with one question — If there’s a simpler way to get things done, why shouldn’t it be explored and used? This is where language interfaces come into play.

There has been a 360-degree shift in how people approach execution. This is a world that clicks less and talks more. People expect to interact with their system instead of pointing their mouse and clicking. That’s the inception of using language as an interface.

Natural language interfaces allow the user to interact using written or spoken ‘human’ commands instead of computer language. Words are used to execute functions such as creating, selecting, and modifying data.

Natural language interfaces can, however, be difficult to use effectively due to the unpredictable and ambiguous nature of human speech. Variation in tone and accent can lead to misinterpretation. Now let’s take a deeper look at the most common advantages and disadvantages of language interfaces.

Pros

Ease of Use: The user need not have coding capabilities or advanced computer knowledge to execute tasks
Better Adoption Rate: Since task execution has become easier, users are keener to try and adopt this into their daily routines
Cost Effective: Debatable point, but if we consider the human capital required to create complex menus with dropdowns and multiple actions, language interfaces cost less
Intuitive Responses: Language interfaces have their base in AI and machine learning. So the system learns to respond intuitively based on the context of the speaker
Safer Alternative: In situations where the user is driving a car, language interfaces enable hands-free options thereby improving the overall safety of the user

Cons

Ambiguity of Questions: The system needs rigorous training to understand the context and even then it can fail as human vocabulary and speech patterns are varied with different connotations and meaning
Conversation Flow Control: It’s a human tendency to have follow-up questions. Users can easily pose questions or give commands that are beyond the ability of the system to interpret. This is in contrast to a menu-driven system, in which the system is always in control — as it constrains the user to select from a limited number of choices.

Natural Language Interface in Real Life

The term natural language interface always inspires the examples of, Siri, Alexa, Google Assistant, or Cortana which allows you to interact with your device’s operating system using your own spoken language.

But the applications are varied from just asking the system for a weather update or whether the traffic is too congested on your road to work. We examine four important use cases of language interfaces in real-life scenarios which leave a lasting impact on the world of business.

Fraud and anti-money laundering

Applying language interfaces to the narrative component of suspicious activity reports can assist with anti-money laundering compliance. Internal audit teams can also gain new insights into fraudulent activity.

Compliance

Compliance teams can automatically identify the most interesting and important information trapped in structured data with language interface systems. It helps them produce language that provides situational context, explanations, and potential next actions.

Forensics

With forensic investigations, natural language interfaces, combined with visual analytics, can reveal insights around anomalous information quickly, which helps target areas for further investigation.

Data Analytics

Language interfaces are also entering the world of data analytics replacing traditional self-serve GUI driven analytic tools and empowering different business teams with the ability to Talk to your Data®.

Looking to the Future

Natural language interfaces are slowly being integrated into the majority of applications, however, it might probably take some more time before the interfaces based on natural language will come into common use for good.

There is no doubt that along with the development of tools for creating bots, it will be easier for the world to create more complex and sophisticated interactions with users, in which the impression of talking with a computer algorithm will disappear along with the differentiation between humans and machines.

The future is not dystopian. It’s great with humans and machines coming together to create a better world.

But wait. It doesn’t end there. In the next article in this series, we will look a little bit deeper into the evolution of user interfaces along with some daily yet interesting use cases of voice AI.

If you’re thinking that text-driven interfaces can go out of fashion, this article might help answer the query.

Originally posted: Hello, World — Language as the New Interface

10 Key Features that Make a Business Intelligence Tool Work for You

Hari Narayan G — Thu, 01 Dec 2022 03:00:24 +0000

A BI tool will radically improve your business operations, but only if it’s the right tool for your organization. Choosing a BI tool can be a lengthy process since the BI tools market has become more fragmented and complex, making it difficult for businesses to make the right choice. There are hundreds of BI tools available today, each promising to deliver value and insights faster with less effort. This article will talk about some of the features you should look for in a business intelligence tool before choosing one for your business.

A good BI tool can help your business achieve better visibility into its data, leading to faster and more accurate analysis, streamlined reporting processes, and improved decision-making capabilities. A business intelligence (BI) tool is an application through which companies monitor the performance of their operations to achieve business goals. As such, a good BI tool should have specific functionalities that enable this monitoring and analysis.

What Data Will the Tool Track?

Choosing a BI tool requires you to choose which data sources you want the tool to connect to. This is often the first thing to think about when choosing a business intelligence tool.

· What data sources does the business intelligence tool connect to?
· What connectors can be used to access your organization’s data sources?
· What data formats does the system accept?
· What will be the data flow into the system?
· What are the steps for data processing and enrichment?
· What are the ways to add additional data sources?

The more data sources that you can connect to your BI tool, the more robust your analytics will be. Here are a few characteristics to look for in a BI tool when you consider adopting one for your organization:

1. Robust Analytics

There are a few ways that you can determine if the tool offers robust analytics. You want to be sure that the analytics is comprehensive. This will enable you to track almost every piece of data that is important to your organization.

Firstly, you have to understand what metrics and KPIs the tool tracks. Secondly, you can look at the user interface to see if there are easy-to-read graphs and charts. As part of your due diligence, you need to analyze user testimonials and past performance with competitors or substitutes in the same domain or industry.

Analytics isn’t just limited to numbers, either — they can also include text and images. With the help of analytics, you can also track sentiment and automatically detect the emotions of the customers by analyzing text data. This will enable businesses to quantify all unstructured data and use data visualization tools to transform text data into valuable customer insights.

2. Data discovery and exploration

The main challenge in data discovery and exploration is to find the data quickly and easily. A good BI tool facilitates this by providing an intuitive UI that requires no training to use. It enables you to search for data, visualize the result, and apply basic transformations on the fly.

A good BI tool also enables you to apply a variety of filters to select the data of interest, and to explore it in different ways — such as by charting it, creating heat maps, or applying other visualizations. With the right BI tool, data can be easily referred to and shared.

3. Bring context into your data

With the help of inbuilt features in BI tools such as data visualization and text analytics, you can bring more context to data. Here are a few to consider:

Data visualization tools — Data visualization tools are excellent options for helping you bring context into your data. Many offer a wide selection of visualizations that let you visualize data in many ways. They also often feature drag-and-drop functionality that lets you quickly create visualizations for different purposes.

Text analytics tools — If your data is in the form of written communications, text analytics tools can help you bring context into your data. These tools work by scanning written communications for key information, such as customer sentiment and other data points. They can help you quickly understand the general sentiment of your communications or dig into the details of specific communications if necessary.

4. Rich visualizations and analysis

BI tools allow you to visualize your data in a variety of ways like different chart forms, graphs, and heat maps. Visualization is crucial to helping you understand your data. It allows you to see patterns and relationships, identify potential issues, and make decisions based on the data without having to manually analyze it.

A BI tool is also equipped to perform various types of analysis on your data, including simple calculations, such as average values or percentages, as well as more complex ones, such as forecasting future values or extracting useful insights from your data.

5. Real-time monitoring and analytics

A critical aspect of any BI tool is its ability to provide you with real-time monitoring and analytics. Tracking key metrics and KPIs in real-time is crucial in gaining visibility into the performance of your operations and identifying any issues as soon as they occur. For this reason, BI tools allow you to set up real-time dashboards that display metrics and KPIs, such as sales metrics, inventory levels, profitability, and more.

You can set these up to display either manually or based on triggers, such as changes in values, providing you with real-time insights into your operations. BI tool should also allow you to dig deeper into any metric or KPI to explore related data, such as previous trends, or to perform more advanced analysis on your data.

6. Built-in APIs to communicate with other Systems

An API can help you integrate your business data with other systems and applications. An API can also simplify pushing new data into your business intelligence solution. If the tool that you’re considering has an API, it will be much easier to connect to other systems.

This can save you time and money in the long run. If you are using an on-premise solution, it is unlikely that the tool will have an API. This doesn’t mean that it isn’t a good solution — it just means that it won’t be as easy to integrate with other systems. If you are using a SaaS solution, it is likely the tool will have an API.

7. Data Governance and Quality Assurance

A good BI tool should allow you to set up data governance rules to ensure that the data you’re working with is of high quality and consistent. You should be able to set rules to identify potential issues in your data, such as values that lie outside acceptable ranges, an excessive number of null values, or repeating values.

The tool allows you to identify and correct issues with your data in real time, helping you to improve its quality. This includes correcting incorrect values, identifying duplicate records, or removing null values. It should also include functionality to identify potential issues with the structure of your data, such as tables with missing or inconsistent fields. It should also allow you to clean up your data, fixing issues such as the ones mentioned above.

8. Ad Hoc reporting

A BI tool should allow you to create ad hoc reports whenever you need them. This should be easy to do with a few clicks, without needing to set up complex reports. A good BI tool should also allow you to schedule reports, providing you with a way to automate the reporting process. This includes creating reports that are scheduled to be generated regularly and sent to different stakeholders who need them.

You should also be able to select the data to report on, and then select the visualization type and configure it according to your needs. This should allow you to create visually appealing reports with little effort.

9. Collaboration and automation

A good BI tool should have functionality that allows you to work collaboratively and automate repetitive processes. This includes creating a central place where you and your team can collaborate, review reports, and share insights. It should also allow you to perform ETL (extract, transform, and load) operations to integrate different data sources into a single system.

A BI tool should allow you to automate repetitive processes, such as data manipulation, report generation, or visualization creation. This should be easy to do by using simple rules, such as “If this happens, then do that”. You should also be able to create custom functions and formulas to perform more complex tasks.

10. Comprehensive Visualizations

A good business intelligence tool should allow you to create comprehensive dashboards that provide a high-level view of your business in a visually appealing format. Dashboards should combine various visualizations, such as charts and graphs, as well as information from other sources, such as KPIs and company data, to provide an overview of your business. A good BI tool should also allow you to customize your dashboards to suit your team members’ needs. This includes selecting the data to include, choosing the visualization types and the way they are presented, and configuring the dashboard according to your preferences.

Conclusion

Choosing a business intelligence tool is a big decision for your organization. You want to be sure that you’re choosing a tool that will best help you to track important data. It’s important to consider all of these factors when choosing a business intelligence tool.

You want to be sure that the tool is the right fit for your business. When all of these factors are taken into account, it will be much easier to find the right business intelligence tool for your business.

Originally published: 10 Key Features that Make a Business Intelligence Tool Work for You

Modern Self-Service BI Tools – Are they still Self-Service?

Hari Narayan G — Thu, 01 Dec 2022 02:27:01 +0000

Businesses are hoarding mountains of data. Data is collected from every resource over the Internet. But most companies are struggling to convert that data into actionable insights. In many cases, it’s because their analytics software is not user-friendly.

All your activities irrespective of whether you’re a marketing leader or a finance head, are dependent on data-driven insights. Data comes from a variety of sources like web analytics, internal sources, ERP tools, and more. The need to find the right insights, reports, and data to aid you in data-driven decision-making is huge.

Here we’ll look at modern self-service analytics, a paradox in itself that claims to be self-service, whereas reality is quite far from it. But before we delve deeper into analyzing the flaws of the subject, we need to understand what is self-serve analytics and how it differs from traditional BI tools.

A Tale of Two Business Intelligence Approaches

Business intelligence is a technology-driven process for analyzing data and presenting it in a format that is easy to understand and use.

Gartner defines self-service analytics as a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support. Self-service analytics is often characterized by simple-to-use BI tools with basic analytic capabilities and an underlying data model that has been simplified or scaled down for ease of understanding and straightforward data access.

The main difference between business intelligence and self-service analytics is that business intelligence is dependent on internal IT support while self-service analytics allows users to access and analyze data on their own. The result in both cases is presented in a variety of formats, such as charts, graphs, and reports.

The difference between both can be easily explained with the process followed by the two approaches.

Traditional BI

Business user comes out with the requirement for the report or dashboard
The user submits his or her request to the IT department
The IT team extracts the required data and then loads into it a warehouse for analysis
Based on the user requirement, the IT team creates the data visualization
Business user approves the report/dashboard or requests changes with the IT team

Self Service BI

Business user approaches the IT team for the set of relevant data to build a report/dashboard
IT team extracts, munges, and presents the data in the required format ready to load into a self-service tool
Business users upload the data into the self-service tools and start querying to organize data for the report
The business user builds the report or dashboard as per requirement using a simple point-and-click action
As you can see the responsibility matrix shifts to the business user’s end in the case of self-serve analytics tools. However, has it happened based on the intent is something that’s waiting to be seen.

The Story Behind Modern Self Service Tools

The current adoption rate of Business Intelligence tools is as low as 35% — Gartner

The need for self-service analytics was born out of a noble intention. Reduce the dependencies on internal IT departments to pull data insights and empower end users to create customized data visualizations with point and click GUIs.

Simply, Power Back to the People.

Alas! The current state of organizations investing in self-service analytics tools has a different story to tell. The process of creating dashboards has become so much convoluted that each of these organizations is forced to invest in specialized teams just to derive insights.

In the words of one of the most revered DC characters, Harvey Dent

Right from technological skill requirements to scalability, there are potential problems with self-service Business Intelligence (BI) which we will investigate further in this blog.

Top 3 Areas Current Self-Service BI Tools Need to Address

We have so far spoken about how self-service analytics is different from traditional BI and the story of its inception. But the lingering question and the topic of this blog is, are self-service tools truly “self-service”? This is where our thought process takes a different route, as the existing self-service tools have the following top problem areas that don’t deem them as self-service.

Ease of Use

Even though modern day self-service BI tools started out to reduce external dependencies, the intent digressed into an undesirable format. Currently, these tools demand a certain degree of expertise that requires training, certifications, and more. The complexity of operating these tools exponentially increases with the amount of data being collected and processed. It inevitably ends in the hands of the IT department to sort through and produce relevant dashboards or reports.

A few organizations have tried to work around this problem by including top business users in IT or the other way around. Introducing IT professionals to business units or functions depending on the size, but at the end of the day, having this approach defeats the purpose of being self-service.

Additional Overheads

As explained previously, some of the modern-day tools demand specialized skillsets to create reports and dashboards. Some organizations have gone ahead and set up a team of data engineers. In cases of large enterprises with access to strong budgets, they have even gone to the length of creating specialized business units to handle the data practices of various functions.

This all boils down to overheads in terms of compensation and benefits which adds up to the huge license fee shelled out to use these products.

Time Loss

Most self-service analytics tools take months and years to master. Even then for a seasoned user to create dashboards and reports, it will take him or her a specific amount of time. For momentary information needs the current suite of self-service tools is not sufficient.

Now to add to the woe, most of these tools create insights based on historical data. This implies for every weekly, fortnightly, or monthly meeting needs a fresh set of dashboards or reports to be produced. This will add to the already existing time delay which seriously hinders the decision-making process for business leaders.

The Way Forward for Self Serve Analytics is Conversational
Gartner believes that moving forward, the dashboards will be replaced with automated, conversational, mobile, and dynamically generated insights customized to a user’s needs and delivered to their point of consumption.

For self-serve data analytics to be true “self-service”, it has to center around the user and how convenient it is to access insights. In layman’s terms, modern day self-service BI tools need to let you as an end user Talk to your Data™

Only then can data access be simplified, and users get customized visualization capabilities.

Is there a way for end users to converse with their data, and derive insights for the momentary data questions they have? Specifically, in the language, they speak?

Meet Kea — Our Smart Virtual Data Assistant. Kea tries to simplify data access and bring clarity via a simple conversational interface. Train Kea on datasets of any size and variety to get straight answers to your questions on data, without the need to sift through complex reports, dashboards, and metrics. See Kea in action intuitively pick out the best way to visualize data and present it to you as an end user.

Originally posted: Modern Self-Service BI Tools — Are they still Self-Service?