Data products are basically designed to enhance productivity. However, the true value behind such products cannot be properly established without knowing what data products are and their purpose within companies.
So, what is a data product?
Data Products and their Analytical Capabilities Source
A data product is an application or tool that leverages data and statistical algorithms to improve decision making processes and aid people making business decisions.
Data products help businesses make informed decisions by providing them with the necessary information and insight from data. This, in turn, gives them competitive advantages in serving and providing for the needs of their customers. Examples of data products are Salesforce’s Einstein AI, Google Analytics, Tableau Prep, and several others. All of these tools make their users more productive.
It is interesting that data products make people and organizations more productive in their day to day activities. The secret behind these products revolves around how data products arrange, organize, and present data to give insight that helps in making decisions. Improving productivity revolves around sorting, estimation, prediction, recommendation, automation, and many other abilities of data products, which are explained below.
Data products ensure productivity by sorting information provided to users. That means, they group items or people based on their common feature(s). This arrangement ensures productivity by making dealing with such information easy for users. For example, reviews for a product may be grouped into “excellent”, “fair”, or “poor” based on the number of reviews from the users. Sorting the reviews in such a manner makes it easy to discover excellent, fair, and poor reviews from users without wasting time.
Racking items and user activities is another secret to improving productivity using data products because it gives insight that helps individuals and organizations make quick and informed decisions.
For example, different products from a company may be ranked according to their likelihood of contributing to sales or the number of the pieces sold within a given time. Thereafter, decision makers can decide on which product to focus on or kill to satisfy the interests of their customers and maximize profits. Ranking, in this case, ensures productivity by reducing the effort and time spent in making decisions. The necessary insight is already provided by data products.
Data products can identify opportunities based on data and statistical decision making algorithms to make suggestions for the end users. Such suggestions are backed by data and can enable product managers, user researchers, and business leaders to identify the wants and needs of their users or the challenges of the organizations. They will be more equipped with insight backed by data to make informed decisions.
For instance, a data product may recommend categories of customers that the company needs to pay more attention to based on purchases on their platforms. This suggesting is another key way to improve productivity because it makes it easy for decision makers to decide what actions they can take to improve their business.
Estimating values of activities on a platform provide useful information for the decision making process. A data product can estimate several factors such as demography, location, language, interests, interactions, engagement and much more to provide in-depth details and valuable insight to support the decision making process of an individual or organization. Providing a detailed estimate supports decision makers to be more productive in process and outcome. Data products such as Google Analytics provide estimates on the traffic of websites.
A data product can make predictions backed by data to inform product managers, user researchers, and business leaders about possibilities they might not be aware of. Such information will help them make informed decisions to achieve productive processes and outcomes. It could predict actions and reactions—churning, fraud, buying, selling—of certain groups of users based on their characteristics, interactions, age, location, unusual activities, and other factors. Such predictions could help decision makers to be more proactive against possible occurrences.
The abilities of data products to sort, estimate, recommend, and predict possibilities necessitate decision automation. Some decision making processes could be automated to free up human intervention. These decisions could be to logout users that are suspected of performing unwanted activities such as spam and fraud. Facebook uses such automation to lock out suspected accounts.
Effective decision making automation helps decision makers get more time to deal with more important decisions that require human intervention, leading to an improvement in output.
As the business terrain is getting more competitive and business activities becoming more complex as a result of growth, it is necessary to have access to data and statistical insights to make informed decisions.
Sorting, estimation, recommendation, and the other methods mentioned above are the secret to improving productivity using data products because they provide us with the necessary insights that enable product managers, user researchers, and business leaders to be more productive in decision making processes and final decisions.