DEV Community: carlo-moretto

How to turn uncertainty into opportunity

carlo-moretto — Fri, 31 Jul 2020 19:00:06 +0000

Introduction

2020 will be a year we will all remember. Covid-19 has forced us all to live our daily lives in uncertainty and the economic downturn that will follow is now very difficult to ignore. In the previous downturn, statistics show that only about 25% of the companies managed to grow their market value. For some companies, markets shifts have created a surge in demand and a springboard for their business. Despite that, for the majority, stalling was the only viable option. But even if you find that your company is now facing this issue, you should not worry because the game is not over yet! We all know that for a business, staying at home for over a month could even result in failure, but, with the right strategy in mind, you can seize this opportunity to regain some ground by means of the right tools. For instance, access to Business Intelligence tools can help you make more informed decisions and navigate more effectively through less stable market situations to uncover opportunities that may not be apparent to the naked eye.

So let’s focus on a good way to make this all work out for the best. In order to recover from an event such as this one, a business should work on a 3 phases strategy: React, Realign and Rise.

React:

Once the event takes place, a business should re-evaluate its plans for the near future. A great way to do so is by observing closely how you have been impacted and take actions in light of your findings (internal benchmarking). Surely it is not easy to understand what future consequences an event might have, but it is a vital step in order to find possible future opportunities and prevent disastrous outcomes. In this step, the majority of businesses will probably end up stalling with the hope of everything going back to normal quite soon and this might be what you are thinking just now. The prospect of going back to our old lives as soon as the vaccine is found is surely appealing, but how can we know when that might be? And what if it never happens? This is why instead of waiting around you should choose the proactive approach and go to phase 2.

Realign:

Once the event has been analysed and an initial possible impact has been assessed, a business should re-evaluate its plans for the distant future. According to recent researches, less than 10% of the companies succeed in achieving the desired growth after a stall. Making sure that you fall into this category might be vital for your business, but the truth is that, as everything in life, it does not come easy. Playing it safe can actually be risky business in a situation like this one. As crazy as it might sound, according to market research, the right move seems to be that of investing. By investing on the right tool, you will be able to gain an advantage over your competitors while also saving money and, in times like these, this could make a real difference in the long run. This is particularly true when the tool can help you predict new trends and understand what is going on in the market, giving you an overall view on the phenomenon.

Rise:

Once this step is over and your business’ finances have been reallocated in the right areas, the only thing left to do is to invest and keep up with the good work. Implementing long-term solutions is not a quick process, but if you follow a solid strategy and you choose the right remedy, it will be a smooth one. If you think that Business Intelligence is what you need and you want to start getting all the insight you need on the current market trends, get in touch with our specialists.

Why agrEGG?

With agrEGG you will not only have a great Business Intelligence tool, but you will also be able to rely on our experts’ guidance for its implementation. Your business will be able to get back in the game in no time and thanks to Machine Learning and Artificial Intelligence, it will be stronger than ever.

Thanks to our support, you will find that it is also very easy to use and highly customizable so what are you waiting for? Make the most of this experience and choose the right tool, choose agrEGG.

Benchmarking: what is it? A case study of insurance companies during Covid-19 pandemic

carlo-moretto — Thu, 02 Jul 2020 16:10:39 +0000

Introduction

Benchmarking is an interesting and unique method for evaluating your company performance or products. With benchmarking you can compare how you are doing, according to a specific indicator (benchmark), with direct competitors or with companies considered among the best in your sector.

Once the performance of your company has been assessed in the context, the purpose of this type of analysis is to calculate the distance from the top of the ranking and, therefore, to search for models or actions that can improve your business efficiency in order to reach or outperform your competitors.

It is very useful to use benchmarking to get a more objective idea of your performance, but let’s explore why in the example below.

Let’s consider an increase in sales of one specific product as our indicator to analyze: maybe our company registers a + 3%. This data alone could make us think that our current strategy is working well, therefore no improvement action is needed. However, if we take into account the performance for that same product of all the main competitors in our analysis, we could find that they register an increase of 15%. In this situation, we should ask ourselves: why is our company not reaching the same sales standards as the others?

With this simple example we have highlighted how the same data takes on a completely different meaning if analyzed in different contexts.

In this article we will talk more in detail about benchmarking. We will focus on the different types of benchmarking there are and on why using it effectively is so crucial during this Covid-19 pandemic. Finally, we will see some features of AgrEGG Benchmarking, a tool natively integrated into AgrEGG that is also available as stand-alone software.

Types of benchmarking

Benchmarking can be divided into four types, each one has different advantages over the others:

Internal Benchmarking: this type of analysis is carried out within the same company comparing different departments or production units. The aim is to identify those who register the highest efficiency in order to emulate their practices in the departments that show worse results. However, you would be limited by the restricted area examined which does not allow a broad spectrum comparison.
External or Competitive Benchmarking: as suggested by the title, this analysis takes into consideration your competitors and overcomes the limits posed by the internal one. With this method you can get an idea of the performance of the companies operating in your same market, but you are also able to gather information on what actions and practices have allowed these companies to reach the top levels. Unlike in the previous type, however, the data for comparison is much more difficult to find since it comes from competitors.
Functional Benchmarking: with this type of analysis you are not necessarily comparing your performance to that of your rival companies, but rather to the ones that excel in a certain category of interest. The objective is to identify which innovative action or best practice has led these companies to excel in that specific category. In this case the companies are much more willing to share their data.
Generic Benchmarking: with this, the best company practices are identified to try and import them into other sectors, even when different from the reference ones. In this case too, companies are more likely to share data, but it is more difficult to introduce the practices identified for inhomogeneities of the sectors.

Covid-19 in the Insurance Industry

To understand the impact that Covid-19 is having on the insurance industry, let’s take into account what the performances of insurance companies have been in the first trimester of 2020 . Data shows that this impact has been huge, to the extent that, amongst other interesting facts, European insurers’ volume of premiums seems to have fallen by roughly 30%.

With this alarming statistics, you can probably see for yourself why limiting your view to the internal data could turn out to be a real threat. Understanding the impact it had on your company alone is no longer enough. Almost everybody is experiencing a significant decrease, so knowing you are one of the many, surely does not help you to find your way out of it. What you need to know now is how your decrease compares to that of the rest of the market. Your goal should be to understand how your company solvency has been affected in a contextualized way, because with Covid-19 it is hard to know what is an acceptable decrease and what is a threatening drop. After you have considered these factors, you will have a better understanding of the situation and you will be able to act on it by foreseeing future consequences and preventing them from becoming an issue.

Focus on Solvency and Complaints

Using AgrEGG Benchmarking tool, our team of experts managed to extrapolate the solvency and complaints data of the main insurance companies. Now, let’s see how a hypothetical company would position itself among its competitors.

Thanks to this graph, not only we can evaluate their performance between 2019 and 2020, but we can also get an idea of how and with what severity Covid-19 has impacted their competitors. Amongst other things, we can also devise intervention measures to decrease the gap between them and other companies or identify actions that do not further decrease their trends.

Moreover, by analyzing the graph above, we can evaluate the number of complaints to extrapolate, for example, information about customer satisfaction and consequently understand which services make the customer more or less satisfied. In such a competitive market, being able to satisfy your customers is of fundamental importance in order to retain them.

As we can guess, without comparing ourselves to our direct competitors, it would be difficult to objectively evaluate our company since internal data alone would not enough to give us an idea of our actual performance in the market; for this reason, benchmarking becomes an essential ally for a complete analysis.

Why AgrEGG Benchmarking for Insurance?

So why choosing AgrEGG Benchmarking for Insurance? In this unprecedented time it became essential to understand how the market is dealing with this crisis, so make sure to be prepared.

If you choose AgrEGG, our team will be extrapolating the data you need from different reliable and verified sources in order to provide you with all the information you need, and it will offer a service of data sharing to always be on top of things.

Monetize your data

carlo-moretto — Tue, 23 Jun 2020 08:10:01 +0000

Introduction

Making your way in the modern market is really a difficult challenge. It is not easy to be disruptive and distinguish yourself from many competitors, even more so in this day and age with globalization. What every company should do is make the most of every resource and keep up with the surrounding environment. In this article we will talk about a particular resource that was almost ignored until recently, but that has allowed the modern big companies to get to where they are today: Data.

Data has become a great source of income. It made it possible to innovate and base corporate growth strategies on every operating sector of the company. One of the peculiarities of data is the simplicity with which it can be acquired, we are talking about the completely free data that each company carries around, but also about data coming from external sources. The latter, can be integrated with the first to gain a complete overview of the main indices of interest, even more so if these are managed and organized with a business intelligence tool such as AgrEGG.

A practical example.

To give a quick and easy example, let’s imagine that we only have the production and sales history of the company’s products for the past five years and let’s try to list what information we can get from it:

it is possible to analyze and transform this data to trivially identify what are the company’s flagship products, in which periods of the year products are mostly purchased, which sectors are most productive or which sectors absorb most resources.

Now let’s try to cross this data to gather more hidden information. We can:

see what is the most efficient and least efficient business sector in order to identify strengths or production gaps and intervene promptly on operating costs;
discover how your target market is evolving and what new opportunities are ahead of you;
understand who the main and best customers are (or identifying cross-selling opportunities);
act on pricing policies;
have all the information and save on the time for management and manual organization of the data;
have a 360 ° view of the entire production processes;
reorganize staff into new teams for strategic projects.

Data & BI.

With this initial analysis, you can easily intervene in all the dynamics just listed. So what can you do when you have different types of data in your company and cross them with external sources? Simple, you will be able to foresee scenarios that were unimaginable up until now.

Having the right BI tool will allow you to simplify the entire process for managing, integrating and displaying data. It will allow you to concentrate simply on where you want to take your company and it will offer you the right means for identifying the changes to achieve it.

Today, all companies have the opportunity to exploit their data with business intelligence and, together with the right tool, they are able to maximize profits and minimize costs simply by unravelling the hidden information that has been stored in their archives for years.

Why AgrEGG?

Thanks to AgrEGG and our team of experts, you will have the opportunity to create new analytical applications to be able to exploit all your data and have the answers you need to meet the expectations of your customers and improve your business organization.

If you want to monetize your data, remember that they do not speak for themselves. Instead of going through the trouble of creating your own business intelligence platform, you can choose a ready-made product, developed by experts in the sector, that will guarantee you get the development of all necessary analytical applications. So, choose AgrEGG.

Machine learning: is it really the best option for you?

carlo-moretto — Fri, 19 Jun 2020 09:36:24 +0000

Introduction

In recent years, machine learning and deep learning have been used as solutions to several problems. Self-driving cars, face recognition, virtual assistants, shopping forecast and even in anti-crime applications. The reason of this success is that although developing and deploying ML systems is relatively fast and cheap, a high performance is still achievable.

But is it really the best solution to a specific problem?

To answer properly to this question we have to consider an hidden factor that can really make the difference when choosing the best technologies for a project: the T __echnical D_ ebt._

Causes of Technical Debt

“Technical Debt” is a metaphor introduced by Ward Cunnigham in 1992 to help understand the long-term costs that can be incurred by moving too quickly in software engineering.

The causes that can lead to one can be intentional – like time constraints placed on development and source code complexity – or unintentional – like lack of coding standards and guidelines or lack of planning for future developments. This debt can be paid off by refactoring code, improving unit tests, reducing dependencies and deleting dead code. However, this is not easy and identifying it requires a careful analysis.

For this reason it is always best to try and set it to a very low quantity from the beginning of the project.

So, what is the main cost of using ML? Primarily, the fact that maintaining them over time is challenging and expensive.

Technical Debt in ML Systems

One of the most common kind of technical debts that arise with ML is _ entanglement _. Machine learning systems mix signals together entangling them and isolating impossible improvements. In this case, a solution could be to isolate ML models (if possible) or to focus on detecting changes in prediction behaviour as they occur.

Moreover, a prediction from a machine learning model is made widely accessible to other systems that can consume the information provided at runtime or later by reading files. Without access controls, some of these _ consumers _ may be undeclared. This problem in software engineering is also know as visibility debt.

Other technical debts you need to be aware of concern _ d ata dependencies , _such as _ unstable input signals _. They are mostly used because of how convenient it is to take signals as inputs from other systems, but they are also unstable because their behaviour changes over the time. A solution for unstable data dependencies is to create a versioned copy of a given signal. Another source of technical debt is _ underutilized dependencie s _, that is unnecessary packages that can be paid off by doing a detailed analysis.

Furthermore, there is reproducibility debt. Machine learning make it difficult to re-run experiments and get similar results.

Conclusions: Measuring Debt and Paying it Off

Technical debt can be measured by asking yourself a few useful questions, like:

• How easily can an entirely new algorithmic approach be tested at full scale?

• What is the transitive closure of all data dependencies?

• How precisely can the impact of a change to the system be measured?

• Does improving one model or signal degrade the others?

• How quickly can new members of the team be brought up to speed?

In addition to this, considering and not ignoring technical debt since the beginning of the project could be enough. These are very good practices that can be very important for the success of the project, his maintenance over time and the team health itself.

Why AgrEGG?

AgrEGG helps to detect and track all possible causes of technical debt. With us, you will be able to test and see all data dependencies in a much easier way.

Also, thanks to our team of experts, you will have the opportunity to receive an in-depth analysis based on your project needs.

References

Hidden Techical Debt in Machine Learning Systems

License and out-of-pocket expenses of Business Intelligence. Which tool to choose?

carlo-moretto — Thu, 11 Jun 2020 08:40:11 +0000

Very often the license cost of a BI tool is confused with the cost of the entire business intelligence process that companies have to face in order to upgrade to the expected data-driven decision-making process. Unfortunately, the truth is that the license alone is not enough to achieve a full transition to a system that allows to use all the data available to reach appropriate decisions. In this article we will see what the real cost of BI is and we will help you to identify the right tool for you.

General overview of out-of-pocket expenses.

To ensure a correct functioning and wide use of your tool, you need to have human assets who put their time into the BI process.

We can divide this staff into:

• Maintenance and tool manager.

• Tool users.

• Responsible for the transformation and loading of data.

• Graphic and design experts.

In addition to the human resources costs, the following will still need to be considered:

• Expenses arising from the addition of space for data growth.

• Time of implementation of new analysis processes.

Below we will go into more detail on the different items listed above to estimate the real cost of Business Intelligence.

Maintenance and tool manager

Generally, it is an IT engineer or data analyst who will be primarily responsible for maintaining and operating your tool correctly in order to ensure its appropriate use in each project. Based on the architecture of the tool and the ease of management, your employee will be more or less busy, hence why the tool you choose can have a major impact. If it is user friendly, it will require less hours of management and maintenance and therefore it will save you money.

Tool users

In this case, the number of resources employed for each project in which business intelligence is used, varies according to the tool. In particular, we can associate the size of the team with the ease-of-use of the tool – directly proportional relationship – and with the number of operations that can be performed independently by the tool itself.

Responsible for preparing and uploading data

The raw data cannot be directly fed to the BI tool. In fact, before loading, the data needs to be prepared and transformed so that it can be understood by the tool. In this case, in addition to human work, investing in an appropriate transformation platform that can ease this process should also be considered. At the end of the data transformation, this has to be loaded and their accuracy must be guaranteed. Therefore, it is advisable to make sure that the proper tool and a data preparation system are equipped with machine learning algorithms to guarantee minimum effort and maximum performance.

Graphic and design experts

At this point it is necessary to consider the presentation of the data that is of great importance in the business intelligence process. The right tool should always allow the use of graphics agreed and implemented in the startup phase and lighten your expenses by guaranteeing you a correct and appropriate design without having to take on the task of employing expert designers.

Data growth

In business intelligence, it is known, data is everything and it is very easy for it to increase day by day as well as the number of external data sources to aggregate and conform to one’s own.

What does it have to do with the cost of BI? Simple, the need to buy new storage space. In this regard, it is important to evaluate both the availability of memory and the allocation methods when choosing your tool in order to optimize both archiving process and response time. A poor tool may require continuous space upgrades which would significantly increase BI spending.

Time of implementation of a new analytical process

Speed is one of the most important variables to really evaluate how efficient a business intelligence tool is. To better understand this point we can refer to the expenses covered in this article and we can relate them to the number of new analytical processes carried out. If we divide the overall cost of one year of BI by the number of projects completed, we can trace how much BI cost for each project. Therefore, speed becomes a very important variable. The more analytical processes have been implemented, the lower the cost of business intelligence will be. It is important that these processes are implemented in the shortest time possible. In order to do this, the right tool should be easily scalable, easy and intuitive to use and the manufacturer should make its expert team available for the reception and implementation of new projects and training of the staff.

Why AgrEGG?

AgrEGG is simple and intuitive, the architecture of our tool and our control algorithms guarantee minimum effort for maintenance and management. In addition, our team is always available to verify that everything works as expected and with high efficiency.

Working with your data has never been easier and the ease of use will allow you to dedicate the least amount of resources for each BI project.

With our platforms optimized for data preparation, the loading process will be fast and extremely simple; you will not have to continuously turn to your IT team and everything will happen in a very short time.

What about the graphics? You think about the result you want to get and we take care of the design.

Finally, our team of experts will be able to identify the data necessary for your projects ensuring the optimization of memory spaces. Above all, our team will always be ready to implement your new processes and ensure you get the highest speed possible in order to allow you to make the most of your data with the minimum expense for each process!

If you are now clear about the total cost for business intelligence, choose the right tool for you … choose AgrEGG.

Business Intelligence and Machine Learning

carlo-moretto — Tue, 02 Jun 2020 09:00:08 +0000

Introduction.

Business intelligence is in itself a very powerful tool that helps and guides companies in the decision-making process; it is possible to analyze all historical data and view them through infographics to obtain a photograph of your company in an easy and intuitive way. However, there is some hidden information that is not so immediate even to analysts and the number of future scenarios that can be imagined by the human mind alone is limited. In this article we will talk about a strong ally that optimizes and brings business intelligence to the next level: machine learning.

A common point between BI and ML.

Business intelligence (BI) and machine learning (ML) both feed on a great deal of data.

The BI process starts from the collection of all internal and external information to manipulate and return it in the form of simple indicators – commonly called KPI (key performance indicators) – or of intuitive and easily readable graphs thanks to data visualization. This way, the analyst in charge can identify the main trends on the basis of the needed factors, typically following a linear data analysis process. If this is already driving companies towards optimized and data driven decisions, try to imagine what happens when a BI tool is associated with artificial intelligence.

Even though machine learning is based on the analysis of historical data too, it does not limit to simplifying the decision-making process on the basis of classic trends. At the same time, it hypothesizes all possible scenarios by making the best use of both the obvious evidence and all the hidden information, reaching conclusions that would perhaps be unimaginable for the analyst alone. Human brains struggle with counter-trend reasoning, but great geniuses do just that. They look for the unthinkable; this is precisely what makes machine learning special. It could be frightening but artificial intelligence, in addition to capturing invisible details, is able to consider what we struggle to imagine such as future scenarios.

What is Machine Learning?

Machine learning can be considered a branch of artificial intelligence: these are automatic learning systems capable of guiding machines (computers) towards decisions not established in advance by the programmer but based on autonomous learning and according to logics not set.

In other words, machine learning algorithms analyze large amounts of data to learn and gain experience from them in order to perform certain functions according to an autonomously developed logic.

The learning process can be mainly of three types:

Supervised; together with an input dataset, the system is also supplied with its relative outputs in such a way that the software can identify a sort of mathematical function to be reused in carrying out similar problems.
Not supervised; the input dataset is ‘without labels’ and the system is left with the task of identifying complex or logical patterns directly on the input data.
Reinforcement; the system interfaces with a dynamic environment and it is acknowledged if the expected objectives have been achieved or if it is in error. This system is more complex, but allows the software to gradually optimize its results and ‘learn’ from its mistakes.

Machine Learning and Business Intelligence together.

The union and coexistence between ML and BI optimizes and completes the data-driven decision-making process: the process is speeded up, the data is automatically cleaned and errors are easily identified, the Insights are discovered, and the information now no longer hides secrets. You can predict future trends and set business strategies that will allow your business to take off. This combination does not only further simplify the analytical operations of your team, but also offers a special magnifying glass with which you are able to grasp and evaluate every single detail or facet of your data. Your decisions will be mediated by new scenarios. You will not only look at the past, but you will have the opportunity to project yourself forward, into the future!

Why agrEGG?

With agrEGG we have focused on evolution. It is not just a common tool for business intelligence, but thanks to the work of our data scientists, you will also have access to the full potential of artificial intelligence.

The ML and AI modules can be easily extended and customized to meet every need, you will have the right tool to guide your company towards new challenges and you will have an analysis system developed by experts in each sector without having your own dedicated team.

Make the most of your data and choose a complete tool with dedicated and customizable ML modules, choose agrEGG.

The importance of data visualization

carlo-moretto — Mon, 25 May 2020 07:12:19 +0000

Introduction

In order to make the results of a business intelligence analysis more effective, it is crucial to be able to present the data in the most effective way so that the users can identify the information they are seeking at first glance. The correct visualization of the data not only allows to simplify the search of information, but makes it much more profitable; studies on the cognitive processes of the human brain show that our memory is able to assimilate information much more easily when it is associated with images or other graphic representations. Moreover, using a color rather than another can affect the assimilation of information in a different way.

In Business Intelligence, being able to represent and shape information so that users can get the most profitable interaction with their data, is of paramount importance, otherwise an impossible navigation is guaranteed.

In this article we will go into some essential features on data presentation.

Choosing the right chart

There are many graphs to choose from to represent the data, but extreme care must be taken to find the right correlation between the data and the graph, otherwise you risk making navigation very difficult.

The bar chart:

It is one of the most common graphs that makes the presentation of data highly intuitive. It can be used to represent trends, trends over time, historical maximums or minimums, to compare different categories or simply to represent volumes.

Pie chart:

Also in this case we are talking about a very used and absolutely intuitive graphic. This is used to represent percentages, proportions between an entire category of data. In general, it is useful to use it when at least two kpi cover at least 30% of the total otherwise the representation may not be read immediately. This is a classic example of visual data measurement.

Line chart:

it is very useful for viewing data that changes over time, such as financial and historical data. Line graphs connect a contiguous series of points where each point represents a single measure. Line graphs are widely used to show up and down data over a certain period of time or to evaluate the area below the line.

Heat map:

the visual reading of this graph is very high, it is generally used to make comparisons on two different categories and evaluate the activation frequency through the use of a colors (typically a single color is used that varies between light and dark shades).

Bubble chart:

this type of graph uses the different size of the bubbles to give an idea of the size of a point measurement. It is often associated with a map to graphically show the areas in which a certain characteristic assumes more or less high values based on the radius of the bubble.

Histogram chart:

It is generally used in the same cases as the classic bar chart but in this case the distribution of a measurement within a group is also assessed: for example, using different colors you can get multiple information in a single view.

Gantt diagram:

it is very useful for planning and evaluating the progress of a project over time, managing to view crucial points such as presentation of deliverables, deadlines, chaining activities and division of tasks.

Scatter plot:

it is used to evaluate the relationship between two variables within a Cartesian plane. Generally, it shows the influence of an independent variable on a second variable, highlighting the effect of the relationship – normally of a non-linear type.

Funnel diagram:

The funnel chart is a type of chart that is often used to represent stages in a process (such as a sales process) and show the situation for each stage. It can also be useful for identifying critical issues in a company’s sales processes. In general, this graph is used when there is a process that can be divided into at least 4 phases and the measurement of each phase is greater than the next one.

The use of colors

As well as the choice of the chart, another fundamental element in the data visualization is the correct use of colors both for technical reasons – such as contrasting with the background – and for reasons related to the user’s emotions. In general, our mind is used to associating a color with a specific interpretation. The most striking example is the use of red and green for negative and positive values or alert messages.

It is therefore necessary to choose them appropriately and use a color that is not in contrast with the message we want to convey, but supports it. It is also necessary not to overdo the different shades to avoid creating confusion and making the use of color ineffective or even counterproductive. In general, uniformity must also be sought to ensure continuity in the interpretation.

The organization of the data.

Another important element in data visualization is the correct organization of data. You must take great care of the presentation because the use of a stunning design is not a synonym of effective interpretation. Adding too much information on the same page must be avoided because the user risks to be overwhelmed and fail to see all the salient data; it is good to organize and divide the data into sub-categories and possibly aggregate complementary information. Unnecessary or unsolicited data must not be presented because it can distract from important information or overload with useless information. The organization of data should be linear and logical both to simplify storage and to be easily navigated.

Why agrEGG?

With agrEGG you will have the possibility to choose among the most disparate graphs, our team of experts will be able to support you in the startup phase of your personalized business intelligence tool … your data will always be presented in the most appropriate way.

We will advise you on the correct use of colors, on how to maximize the effectiveness of learning from the data, and make its interpretation simple and intuitive.

Your data will be organized according to your specifications but in a logical and organized way in order to make navigation easy and guarantee immediate reading.

The design will be taken care of by professionals in the sector and your dashboards will be so captivating that they will make your presentations unique.

The goal of a designer is to listen, observe, understand, sympathize, empathize, synthesize, and glean insights that enable him or her to make the invisible visible.

Hillman Curtis

Why would Business Intelligence Improve your Business?

carlo-moretto — Wed, 13 May 2020 06:53:56 +0000

Introduction

Every day companies must make decisions that can result in a success or a failure, so the best thing is to make smart decisions! To do this, you have to base your plans on evidences, on useful information and on real trends; of course, this involves having a large amount of data… do you have it? You must know, however, that the data alone is not very useful until you can translate it into information. This is why, the business intelligence is the solution that’s right for you and in this article we will explain why.

Do you have a lot of data, but you don’t know how to manage it?

The most common thing for a lot of companies is that they have data coming daily both from within the company and from outside – such as customers or market studies – but we are talking about raw data that does not necessarily allow to make any data-driven decision. Without proper data processing, analysis and interpretation, it is easy to make mistakes or come to partially incorrect conclusions.

Fortunately, today we have a lot of tools that allow us to speed up and optimize the decision-making process so that a company can improve in all strategic sectors. Thanks to business intelligence tools it is possible to organize all the company information directly into their own system to easily understand the needs and preferences of customers, but also forecast upcoming needs, identify the company’s strongest suits and improve business processes.

Why investing on a Business Intelligence tool?

In business intelligence, the correctness of the data and its appropriate manipulation is a crucial step. In order to use statistical mathematical models and in-depth analysis methods, the work of specialists such as developers, data scientists or business analysts is a must. So, if you don’t want to resort to a BI tool, you will need an entire IT team that can handle the whole data mining process. Typically, simple spreadsheet tools are used, but what happens when the amount of data increases? What if you need to integrate different data? What if more people need to interact with it?

The right Business Intelligence tool guarantees cleanliness and correctness of the data without continuously engaging with your IT team. With the right product you will have a powerful tool that will allow you to collect, organize and verify your data with absolute ease, minimizing costs and maximizing performance. You will have the opportunity to integrate various databases and receive the answers you are looking for with a few clicks. The information will be safe and can be found at any time in the simplest way.

Who is in charge of graphics and design?

There are many reasons why it is crucial that the data is shown in the right format. Many studies show that graphs and images make comprehension faster and easier. You have to understand the relevance and context of the data. You have to understand how to best express it. And finally, you have to represent it in the most effective way – everybody knows that aesthetics are important – while still staying true to it. You need all this in order to impress customers or management. To sum it up, you need to put more work into planning the representation of the data and at the same time implementing it with an impressive graphic.

Otherwise you can use a business intelligence tool that allows you to always select the right chart for an easy identification and display of the most important data. Furthermore, you will not need a team of designers because behind your tool there is a highly qualified staff that will help you identify the perfect charts and will take care of satisfying your graphic requests by implementing your favourite designs. In this way you will also have personalized graphics for your company’s data.

A summary on business intelligence.

Business intelligence is a decision-making process that allows you to reach conscious conclusions thanks to an analysis of data that can be translated into the correct interpretation of company information. In other words, we can describe the BI as the process by which business information is processed in order to obtain meaningful data that can guide the strategic development of a company. Generally, we can divide BI into a three-step process:

The first phase involves the collection of data ; both corporate information and external databases can be used (so you can evaluate your company also with respect to competitors).

The second phase concerns the cleaning, selection and aggregation of data ; large amounts of data are always uneven and also not all information is useful, therefore they must be normalized and aggregated in a single data warehouse.

The third phase involves the analysis and presentation of data through reports, dashboards and KPIs.

At the end of the process, the initial information will be transformed into an easy-to-interpret smart data that will allow the company to identify its own strengths, customer trends as well as internal gaps, opportunities or future needs.

Why AgrEGG?

With AgrEGG you do not buy a simple product, you will not be alone in your business transformation. Our team will offer you all the technical and scientific support by delivering a personalized tool according to your needs.

You will have the information you want within a click and you will be able to stand out from your competitors. Our tool is simple to use and does not require any further work for your IT team.

Take your data wherever you want, from your smartphone to your pc… always with you.

We guarantee the cleanliness and correctness of the data thanks to our strengths: machine learning and artificial intelligence. Your data will be presented in unique reports and charts developed for you by design experts.

We have many years of experience in the business intelligence sector and our various collaborations allow us to offer specific products for different areas, from finance to health, through production.

Take the opportunity to fully exploit your potential and enter the world of business intelligence with the perfect product.