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The What, Why and How of operationalizing data analytics

An enterprise may have deployed analytics to read the patterns within its data but sometimes, businesses fail to effectively operationalize analysis. This is as good as pouring money down the drain as adopting analytics is only a job half done; ask any data scientist and he/she will tell you that not operationalizing it (the last step) is like holding on to a bag of goodies but not sharing them, letting the treats go to waste, eventually.

An analyst once rightly remarked – Operationalizing analytics can be done cost-effectively if you implement the right practices and the right people. That’s so true. Once an enterprise has decided to allow data analysts to start studying the data it produces, it must, almost simultaneously, set in place a process wherein the output of the studies can be adopted across the length and breadth of the organization, and by all people within the enterprise who matter in their day to day work.

Largely, analytical approaches are categorized into descriptive or predictive analytics. The first concentrates on what has happened within the organization in the past. The analytical team starts to look at already existing data to try to understand what’s up with the business, also called hypothesis testing.

Predictive analytics, on the other hand, is focused on what is likely to happen in the future, based on available patterns within today’s data.

Vital elements required for operationalizing analytics

Operationalizing analytics requires the building of an analytic model. For this, workflows have to be put in place, standardized across the board, which are also shareable. These essentially have the following components – data mining and preparation, the use of algorithms and high value analytic capabilities, data model planning and building. Data access is built on metadata like data about customers, services or products.

Using all of this, an Enterprise can develop a robust analytic model, especially in the case of predictive analytics. Operationalizing workflows also require a more systematic focus on the use of analytics in an enterprise operational system. A workflow thus developed and standardized, can then be shared to help the analytics team set up the model.

So, two components play a critical role in operationalizing analysis – technology and people.

If an enterprise has not made investments in the right technology, execution of the outcome of its analysis may remain on paper because the tech is simply not there to execute the output.

Human resources of a business is the other vital element in the path of the implementation of analysis. Again, experience shows that if all the crucial department or unit heads are not on the same page where analytics is concerned, the chances of lopsided operationalization are extremely high.

Here’s the way ahead

In this first blog post on operationalizing analytics, we, at Express Analytics, will share with you some vital insights into how exactly to go about the process. While, in this “master” post, we will provide a quick primer to the operationalization model, the posts in the coming weeks will explain in detail, each of the factors that go into building this model, so that our readers get the macro as well as micro picture.

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Operationalizing Analytics requires Enterprises to:

  1. Build a Customer Profile Database: More often than not, this is often the biggest stumbling block. The core input every company needs for operationalizing analytics is a customer database where it can track the interaction of customers across all channels – be it desktop or mobile; sms, chat or e-commerce. Creating a database which tracks the behavioral, contextual, psychographic and demographic activities allows integration of interactions at a customer level. This is critical to building a mosaic of each customer on multiple dimensions. Integrate first party, third party, social and public profiles from databases such as Experian, Dun and Bradstreet – so also from email clients that the Enterprise uses, such as Marketo.
  2. Build a ‘Prospects’ List: Segment ‘Potential Customer List’ using pre-defined criteria. Then, start a campaign. Source keywords searched, products , categories, and styles browsed by customers from the database into the selection criteria. Open and select the profile of each prospect or customer segment and map the list to the suitable profile targeted.
  3. Build a Rules Engine: To execute the stipulated business rules of an Enterprise, what is required is a Rules Engine. Business rules combine facts and conditional statements. Eg: If Group A of customers is found to be responding to email every week, while Group B is found opening emails twice a week, then create a triggered response to both these groups by sending them lucrative offers over email at the designated frequencies. Customization of the rules can be done from a unified dashboard.
  4. Segmentation of Customer Base: Businesses will always deliver better if they know their customers at an individual level. Every day, volumes of data from and about customers is collected which can paint their true picture. Data on their lifestyle, interests, purchasing decisions, past behaviors, geo-location, and where they look for product information can be collected, along with the social media signals put out by them, and the commonality in these can then be used to predict their future behavior.
  5. Monitor User Experience: User experience (UX) means having a deep insight of product users, and includes factors like what consumers crave for, their abilities as well as their limitations. It also means improving the quality of the user’s interaction with a product or service. Visual interaction abilities include such as self service visual data exploration, and list creation and selection. For each selection criteria, one needs to create a Venn diagram. Then, using multiple overlapping Venn diagrams, an Enterprise will be able create a set of consumers with commonalities (those who will be found in the overlapping parts of the Venn diagrams.)
  6. Real time Tracking: Software as a service (SaaS) is used to track user behavior and activity, even as it happens. Such tracking is vital to easily identify trends, for example. In addition to following individual user session, Enterprises can analyze by activity or user segment to better understand user behavior and application performance. The real-time user monitoring capability uses client side libraries to gather event data from a user’s computing device.

The above 6 factors make up what’s called the operationalizing of data analytics. In the coming weeks, I shall be providing deeper insights into each of the above factors. So stay tuned.

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