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From Data To Decision: Why companies fail to benefit from such a long journey

Can you name that one big challenge that businesses which have deployed data analytics face?

It’s not failure to get qualified data scientists or IT professionals, nor is it finding the right analytical tools. The problem confounding companies is – how to implement actionable insights derived from analytics.

For businesses that have started deploying analytics, the journey starts with data, moves on to its collection, analysis and visualization, finally ending in a decision that has to be then implemented. Yet, like a decathlon athlete who fails to clear that last hurdle, many companies falter at the last mile.

Realization of value from data to achieve business goals has been a challenge all these years. It continues to be so even today. For successful implementation of business intelligence, the last mile access to company executives remains of utmost importance, especially when it comes to delivering actionable insights to the team.

So how can a company develop a system that supports data-driven decisions?

For the past decade or so, businesses have done it using “conventional” analytics. But even after that, many companies today are still not realizing the ROI they expect from their analytics solutions. Most suffer from the last mile malady – not being able to convey the value to end-users. After all, end-users are expected to use the analysis to resolve problems, but if they don’t know how, well, what’s really a valuable diamond looks like a piece of worthless glass. All that data might as well be junk information.

There are a handful of reasons for this.

End-users are still not being trained to recognize that diamond. They are not adequately taught to understand aspects of analytics such as the limitations of analytical models, or the propensity towards logical biases in judgmental heuristics. The latter are methods by which one makes assessments of probability simpler. Data analysts themselves must also be more meticulous in applying the right analytical models; their analysis must be accompanied by caveats, and explanation of the risks to end-users.

Another reason is the non-availability or the high degree of difficulty that executives in a company face in reaching the value derived from data analytics. Flow of this information has to be easy to the relevant team members; all those involved in decision-making. It’s like a river, where all types of people can come to the banks and drink, at any point, at any place along the route. In fact, it has to be as easy as downloading the information with the touch of just one key on your computer keyboard. Yet, often it is found that access is a cumbersome process. that does not happen.

As every data analysts will vouch for, there are two inherent road blocks they always face in the data analytics journey – the first mile and the last mile.

With the explosion of content and its distribution, the integration of data represents a major challenge today. How can an Enterprise collate and assimilate such copious amounts of data, gathered from so many different channels? And, how does it do so in real time?

The last mile challenge, on the other hand, is, how to extract actionable intelligence from this pile of refined data and implement it in the decision-making. Without the wherewithal to do this, your data may end up being nothing but useless information.

Challenges

Some of the hurdles that companies that embrace analytics consistently face are:

  • Limited budget
  • Lack of technical knowledge
  • Ever-increasing data sources
  • Staggering pace of analysis
  • Failure to give end users tools to access derived value

Solutions

To tackle the last mile challenge, organizations need to plan and operate on two levels – short (read daily) and long-term. The needs of an Enterprise on a daily level are different from those at a monthly, quarterly or annual level.

One way companies are going today is deploying Artificial Intelligence (AI) in resolving some of the above-stated issues.

Organizations have started using AI in scraping the web, which is an-ever burgeoning repository of data. It’s this vastness that poses the challenge to navigate through unstructured pile of information and extract it. It takes a lot of time and effort to scrape data from the web, even with the advanced web scraping technologies.  Researchers from the Massachusetts Institute of Technology (MIT) recently released a paper on an AI based- “information extraction” system that helps turn plain text into data for statistical analysis

Businesses need not only to work smarter but even faster in order to gain from the insights derived from data. Time is of essence today for many businesses, in order to translate data-related value into results.

Whether in logistics or retail, by having access to analytics on-the-fly, your company can leverage advanced end-to-end delivery, helping the front and back-end, in the process. A logistics company, for example, can use data related around the routes its delivery trucks take, coupled with a customer’s profile, to generate the most optimal route for the delivery vehicle. When that’s done and then conveyed to the driver and the customer (expected delivery time), it will result in better customer service.

By combining analytics and real-time data, companies can plan almost down to the minute, in terms of demand and delivery, and optimize the delivery routes, thus improving on the number of orders, fuel costs, and simultaneously minimize the cost of getting returned items.

The key to success for organizations in coping with the last mile problem is:

  • Enforcing data quality policy
  • Leveraging technology and people to support company policy
  • Providing easy access to crucial team members to analytic insights

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