What Do We Mean by 'Data Driven Business'?

Andy McCartney's picture
 By | juli 19, 2017
juli 19, 2017

We just finished work on a new video that introduces data-driven business.  It is a trending term for sure, sometimes misunderstood, often misused. In our industry we see it as how organizations are trying to increase adoption of business intelligence (BI) and analytical insight to employees, partners, and customers. The primary purpose is to execute planning and business based on data, not intuition or guesswork.

So what is the biggest challenge with increasing the degree of data-driven business undertaken by organizations?

Some might say that the BI and analytics technologies available to employees works for some, but not for others. The data discovery tools that have risen in prominence have proven valuable for those who are analytically natured, reasonably data savvy, and willing to learn a new tool. Others do not have the time, skills, or inclination to learn or figure out how to use a new technology. The tools themselves have become more intuitive, but they have also become more complex as more features and options have been packed into them.

The role of data scientist or citizen data scientist has increased in prominence, as more advanced analytics are possible by non-technical workers. Younger, more tech savvy workers will undertake more data-oriented tasks, but we are still faced with 70-75 percent of the addressable user population without data-driven business.

Take a look at the video for a few observations and thoughts on how to approach this challenge.

Here are some points worth considering:

  1. Trust in data. This is an absolute fundamental, because if your organization cannot provide accurate, trusted, and timely data, there is no point in analyzing anything. We are amazed at how many decisions are made leveraging questionable data. Providing trusted data is, however, easier said than done, especially when attempting to incorporate new data sources, and then trying to solve data integration / data quality problems. This page might help on this topic.
  2. Provide BI and analytics on users' terms. There are several metaphors that can be used when it comes to delivering inappropriate tools to the wrong users, e.g. "Square peg round hole", "Horses for courses". The key is to provide the data that helps with users' activities in a way that is convenient, intuitive, flexible, and, of course, valuable. Our organization is widely known for going beyond the 20-25 percent adoption for execs and analysts, and supplying a wide range of tools, applications, and analytical documents to the 75-80 percent as well, whether everyday operational workers, or business partners and customers outside the firewall. We have been solving this challenge with a combination of 'tools' and 'apps'. More info here.
  3. Customer-facing analytics. Another way to increase adoption of data-driven business is to deliver analytics outside of the firewall, in this case to your partners and customers. You could do this via some kind of portal, extranet, or information application that has a live connection to your analytics platform. There is another type of customer-facing analytics that is incredibly cost-effective, based on a patented technology we have developed called In-Document Analytics. This enables you to automatically create analytical documents that each contain reports / charts / dashboards, the data, and analytical functions all within a single document, providing interactivity even when disconnected. Also, be sure to explore the capability known as Data Monetization when looking to generate additional value from your data. This page elaborates further.
  4. Consider new sources of data. As big data enters the mainstream, many new sources of data are being introduced into the information equation. New data sources open up a world of opportunity, not only for user-oriented decision-making, but for the automation of operations based on predetermined or machine-generated rules. Examples might be Internet of Things (IoT) sensor data, or unstructured sources like e-mail, audio, or video. You may have seen the graphic stating that 90 percent of all data by 2020 will be unstructured. How can we leverage that data for competitive and operational benefit? Our first task is to consider the relevance and possible value of this data. Then we can figure out how to integrate all our data sources to provide context and practical value for its use. Here is more info on IoT and Big Data.

I hope this blog has offered food for thought on how to maximize the use and value of all the information sources available to you. Right now our industry is progressing at an unrelenting speed, which is generating both challenge and opportunity. I love it!