Can you name that one big challenge that businesses that have deployed data analytics face?
It’s not a 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 begins with data, moves on to its collection, analysis, and visualization, and finally ends with a decision that must be 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 remains so even today. For the successful implementation of business intelligence, last-mile access to company executives remains paramount, especially for delivering actionable insights to the team.
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 still do not realize the ROI they expect from their analytics solutions.
Most suffer from the last-mile malady – not being able to convey value to end users. After all, end-users are expected to use the analysis to resolve problems, but if they don’t know how, what looks like a valuable diamond is actually 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 for simplifying assessments of probability. Data analysts themselves must also be more meticulous in applying the right analytical models; their analyses must be accompanied by caveats and an explanation of the risks to end users.
Another reason is the non-availability or the great difficulty executives in a company face in deriving value from data analytics. The flow of this information has to be easy for 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 a single key press on your computer keyboard. Yet often, access is a cumbersome process that does not happen.
As every data analyst will vouch for, there are two inherent roadblocks they always face in the data analytics journey – the first mile and the last mile.
With the explosion of content and its distribution, data integration is a significant 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 decision-making. Without the wherewithal to do this, your data may end up being nothing but useless information.
What are the Challenges in data-driven decision-making?
Some of the hurdles that companies that embrace analytics consistently face are:
- Limited budget
- Lack of technical knowledge
- Ever-increasing data sources
- The staggering pace of analysis
- Failure to give end-users tools to access a derived value
Why are Data-Driven Solutions Required?
To tackle the last-mile challenge, organizations need to plan and operate on two levels – short-term (read daily) and long-term. The needs of an Enterprise at the daily level differ from those at the monthly, quarterly, or annual levels.
One way companies are going today is by deploying Artificial Intelligence (AI) to resolve some of the issues above.
Organizations have started using AI to scrape the web, an ever-burgeoning repository of data. It’s this vastness that poses the challenge of navigating an unstructured pile of information and extracting it.
It takes a lot of time and effort to scrape data from the web, even with 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 also faster to gain from data insights. Time is of the essence today for many companies to translate data-related value into results.
Whether in logistics or retail, with on-the-fly analytics, your company can leverage advanced end-to-end delivery, bridging the front and back ends.
A logistics company, for example, can use data related to 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 for demand and delivery, and optimize delivery routes, thus improving order volume, reducing fuel costs, and minimizing the cost of 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
