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The Data Challenge

There are many examples, particularly in a given department or area of a business, where this is true. Most organizations have internalized the need to better leverage data and have data programs underway. Often times labeled “data enablement” or “data lake” programs all have collecting, organizing, and leveraging data at their core. Many more organizations know there is a need and are exploring their options. All the above are finding it is much easier to theorize the benefits than get a program started, let alone drive a return on their investment in their data-centric program.

To make matters worse, the increasing security threats and continued tightening of privacy regulations provide even more barriers to these programs. In many cases, these two factors have organizations walking back their progress in deriving value from their data. It almost makes the nirvana of data democratization, where all information workers have all of the data they need to effectively do their job, seem impossible.

Although the barriers to delivering on the promise of data are high, with the right approach to both the organizational and technology facets of these programs, there is hope for nirvana. In the coming collection of blog posts, we will discuss the challenges and potential solutions to help your organization deliver on the data promise.

To help provide context for this discussion, let me introduce Traditional Vacuum, a fictitious organization we can follow on their data democratization journey. As the name would suggest, Traditional Vacuum is a long-standing brand in the floor care business. Once the dominant quality brand in its industry, Traditional’s market share has been consistently falling, threatening what has historically been an extremely profitable business.

To shore up the bottom-line, Traditional has implemented a number of departmental operating efficiencies, started selling products direct to their end-customers, and even launched a new connected product. None of these measures have significantly impacted the trajectory of the organization.

Traditional’s management is struggling to understand the impact of recent changes in its sales channels and products. What is clear is that the massive investment in new technology services and partners has impacted their margins and created blind spots in the company’s ability to monitor its performance and adapt. These new channels and products have created new silos of information around the organization, threatening to erode the high quality, customer-focused brand image that has set Traditional apart from its competitors.

Ironically, Traditional has more data than ever before. The new direct-sales channel and connected products have them awash in data, yet they struggle to understand the business more than ever. This irony is increasingly apparent amongst the leadership ranks, which calls the organization to address these challenges.

Traditional finds itself in the quandary that many other organizations have; facing the challenge to unlock insight and drive organizational improvement from these insights. Unfortunately, there is no set formula to address these challenges, as every organization is unique. However, this does not mean that Traditional needs to start their journey with nothing. A handful of general practice areas can be adapted to help guide Traditional on this journey.

Looking at these practices from the highest level, they fall in the following areas:

Organization Enablement – Applying the practices of organizational change management and project management to not only ensure the technology gets implemented but is used to grow and drive value in the organization. Practices such as defining purpose based on business objectives, establishing sponsorship and ownership, and driving alignment and accountability across the organization.

Technology Enablement – Delivering a complete set of technical capabilities to ensure data is accurate, accessible and safe. This includes attention toward the complete lifecycle of data from capture, to use, operational oversight, through eventual retirement.

Risk Management – Applying a set of practices to enable the organization to find a business led balance in the risks of holding and using data (e.g. security and regulatory compliance) and potential value this data can bring to the organization.

Starting a program without considering all of the above areas in scope will likely lead to another failed data program. We see these data programs deliver sub-optimal results most of the time. It is rarely because these programs do not have the proper technology skills on the team or poor program management; they don’t address the entire solution, most of which have nothing to do with technology.

Those in technology careers often look only to technology to solve business challenges, hoping that the latest tools will be the silver bullet that addressed the problem once and for all. After all, the Technology Enablement portion of these programs is daunting enough. But even with the most advanced technological approaches, there is no easy fix. For example, in the data space, data lakes are all the rage and deliver on their promise in a rare number of cases. Some of the top analyst firms have estimated that 85 percent of data lake programs fail. These failures are in no way attributed to the excellent tooling available in this technology space today. The problem lies in that these tools only address a portion of the needed solution. As mentioned before, if all an organization is focusing on is the technology, even the best data lake program will fail to reach its potential.

In upcoming blog posts, we will cover the complete set of disciplines needed to progress an effective data program. You will also get perspectives and advice in applying these disciplines from industry practitioners who have experience in these programs. Coming to understand that creating an effective data program is as much, if not more so, about the human element as it is about technology. And these different roles, with differing perspectives, will provide valuable insight in addressing, Traditional’s and possibly your own data challenges. Stay tuned.

Brian C.

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