Defining Your Investment In Big Data
The Gartner survey of 720 Gartner Research Circle members worldwide, conducted in June 2013, sought to examine organizations' technology investment plans around Big Data, stages of Big Data adoption, business problems solved, data, technology and challenges.
It found that of the 64 percent of organizations that were investing -- or planning to invest -- in Big Data technology in 2013, 30 percent have already invested in Big Data technology, 19 percent plan to invest within the next year, and an additional 15 percent plan to invest within two years.
Does this sound promising? It really depends on what organizational leaders envision when using the word "invest."
Gartner provides some insight here in its announcement of their findings, noting that investments usually go through different stages starting with small investments in time for knowledge-gathering then culminating in a pilot program, investments in the implementation of systems and, ultimately, in their management.
This is certainly a comfortable, reliable, and proven way to usher in change, but savvy marketers will do well to avoid the trap of focusing on technology alone. A solid and growing data program should be built upon these six pillars of digital analytics, which define analytics maturity.
Objectives: Crystal-clear definition of business objectives that build value and can be measured by structured Key Performance Indicators (KPIs) for quantifying success or failure
Governance: Well-defined, well-communicated roles and responsibilities holding teams and people accountable across the full spectrum of activities required to collect, analyze, and use data to measure and act on business goals.
Scope: A well-defined, well-communicated, realistic and achievable set of goals and a road map to drive the organization forward, providing more value at each step of the data maturity curve.
People and expertise: Experienced technical resources and systems architects to keep the foundation running smoothly as well as the analysts, data scientists and empowered business users to drive data-driven decision-making and competitive advantage.
Improvement process methodology: Formal frameworks, such as Agile or (Lean) Six Sigma, in place across teams and departments enabling team members to accomplish their goals and ensure continuous improvements occur throughout the organization
Tools, technology and data integration: Appropriate technology including data collection and analytics platforms plus reporting, visualization, business intelligence, and statistical modeling tools.
Time and time again, we see that the more balanced an organization is across the six pillars of digital analytics, the more likely it is to truly experience a data-driven culture and improvements to the bottom line.
When we released our own survey of digital analytics maturity across industries, we found that many organizations tend to be ambitious in terms of their objective and scope, but lack the rudimentary business processes and methodologies required to follow through. Others tend to over-invest in technology while ignoring the people and resources to use, and still others define lofty goals and aspirations with respect to what they would like to do with data, but have not formalized the governance aspect.
Most organizations would do well to spend time reflecting upon their current state and exploring which steps they need to take across all aspects of maturity to bring themselves into balance and grow. Charting a course of realistic expectations over a two-year-plus timeline is a fantastic way to achieve a higher level of maturity.
Investing in Big Data should not mean merely investing in the latest and greatest technology. Going down that road will guarantee nothing but an organization acquiring a big shiny toy that nobody knows how to use.
Having a balanced plan that prioritizes all six aspects of analytics maturity is simply the most effective way to ensure that all your investments -- of time, money and reputation -- pay dividends by transforming your organization into one that relies on sound data to make important decisions.