Making the case for self-service analytics
Analytic capabilities have been advancing at a rapid pace and are empowering and accelerating the ability of organizations to gain essential transformative insights. These insights are enabling them to sustain, innovate and grow business in a very competitive and pressurized environment.
There are many factors that are driving the need for this capability. Specialized verticals exist in most organizations that require career level focus from a subject matter perspective that is not easily centralized. Prioritization of analytic development across an enterprise is also challenging as there are often competing initiatives. Traditional business intelligence platforms require significant infrastructure and highly skilled labor to manage at scale.
Prior to the recent advancement of self-service tools, it simply took too long to perform the simplest analyses and business leaders were often stuck in a development queue. These factors aside, self-service and independent action with data decentralized across an enterprise can rapidly lead to data chaos and anarchy resulting in analytics that cannot be trusted.
Reigning in or preventing this chaos requires thoughtful strategies that do not impede business units within an organization from effectively managing their areas of purview, nor should they prevent the advancement of their analytic capabilities. There may be a temptation to stick with more traditional approaches that keep tight control on data access and business intelligence tools given the explosive growth in the generation of data of virtually every kind.
It is becoming increasingly difficult to ensure data quality and accuracy while at the same time providing the level of access to data that a business demands. If the objective truly is to enable the business to be successful then a solid strategy is needed. This strategy must enable business units to evolve and adapt to a landscape that is rapidly changing and seeing exponential growth in data generation.
With the proliferation of powerful data visualization tools it is getting easier to produce the kind of outputs that will turn heads and seemingly add value. These capabilities are certainly important to delivering effective analytics and may garner a lot of enthusiasm.
While these outputs can look flashy and official, they may not provide meaningful or actionable insights. This is the key concern for leaders that have responsibility for data governance and/or enterprise analytics in particular. The various platforms that provide this capability are becoming easier to use and do not require the same level of skilled labor that the more traditional business intelligence options.
That being said, regardless of which tools are selected, they cannot relieve the burden of data management and curation. The real work of analytics will remain with subject matter experts within the multiple verticals found in each organization. These tools and technologies can only be truly valuable when the experts can work together to harness all of the available data to derive actionable insights.
Enabling the generation of these insights is not only possible but it can provide a competitive advantage. Organizations have multiple significant disciplines that are generally not interchangeable and all are required to provide the full range of services that are required for success. Within each of these areas there are also leaders and staff who monitor, understand, analyze and respond to data that is generated to support the functions within their purview.
My suggestion is to focus on developing strategies that enable experts in their respective verticals to optimize their functions with effective analytics.
Editor's note: Christopher J. Hutchins will speak on the topic "Taming and Preventing Data Chaos With Self-Service Analytics," at the MDM & Data Governance Summit, July 10 to 12 in Chicago, hosted by Information Management, a sister publication of Health Data Management.