Not long ago, IBM Research published, “Leading Through Connections,” the findings from face-to-face interviews with over 1,700 CEO’s from around the globe. The research compared the answers of CEO’s from the highest performance organizations, to those from average (or below average) performing companies.
Transformation: Data > Insights > Actions > Experiences
The biggest difference between the two groups? Successful businesses are successful at turning data into action.
How have those companies gotten good with data? The top priorities of their CEO’s are to:
- More rapidly identify customer insights in their data and translate those into great customer experiences, and
- Create a more collaborative workplace
In other words, big data makes it possible for companies to know their customers better than ever before and – as a result – to deliver a better customer experience than their competitors. And because of all the skills required to capture, analyze and take action on data, a collaborative work environment is more important than ever before.
Big Data Challenges
When trying to emulate these CEO best practices, organizations are running into a few problems.
It’s no secret that the amount of data available is growing at an explosive rate. What may not be as obvious is exactly how big the data explosion is (see nearby research from EMC’s, “The Digital Universe of Opportunities.”) Organizations simply can’t keep up. By the time one big data project is completed, it is obsolete – lost under another avalanche of big data.
But the amount of data isn’t the only problem. Beneath all of the data is ROT – data that is redundant, outdated or trivial. And, as research from Gartner (“Dirty Data is a Business Problem, Not an IT Problem“) and AIIM (“Automating Information Governance“) suggests, the growth in data ROT is outpacing the growth of data itself and is approaching a crisis for many businesses.
Just like a rotting foundation, data ROT is eroding the long-term value of businesses. Failing to address the problem is only creating a larger problem in the future. Success in business is increasingly dependent upon data, and if data foundation ROT is put off too long, many businesses will find that it is too late to catch up.
Solving Big Data Problems
How are successful organizations cutting a path through the big data jungle?
First of all, they’re recognizing that the big problem is not big data – the big problem is little data. Little data is the source of data ROT in big data. Trying to solve the problem in big data systems is hacking away at the leaves and ignoring the root of the problem. When organizations focus on the little data issues, not only is ROT reduced, but big data growth is slowed (by eliminating redundant and outdated information), which helps to reduce the overall problem.
To extract the greatest value out of big data investments, the leaders focus on four priorities and – spoiler alert – big data is the lowest of the priorities.
Harvard Business Review estimates that workers spend 19% of their time trying to find the information that they need to get their jobs done (“Social Media’s Productivity Payoff”). That’s one day each week. If a company employs 1,000 people with an average salary of $50,000, that’s $10 million spent each year looking for stuff! Making it easy for employees to find information on a customer, colleague, idea or document requires data that is well organized, minimizing ROT, and great technology to make searching easy.
The data that is in most engagement systems (such as customer relationship management, marketing automation or employee collaboration tools) is a mess both in terms of the quality of the data and the ease of using the data. Keeping it clean requires integration, governance, processes and automation. Integration means less re-keying data (creating redundant data). Governance means establishing standards and monitoring quality. Processes means providing the training and SOP’s so that users consistently enter quality data. And automation means minimizing the amount of work that users have to do to keep data clean.
We are living in an increasingly complex workplace. Managing complex individual and team workloads is a challenge. Medium data – visualization of information in little data systems – gives managers and employees what they need to set priorities and evaluate progress.
Last … and least … is big data. If little data is a view of a single story at a time, then big data is the summary of thousands of stories. Three audiences consume big data: (1) executives use high level big data to understand the big picture of the company, (2) analysts dig into granular big data to find subtle trends, (3) little data systems consume feeds from big data to make more data available to the users of those systems.
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Question: What is one way your organization has learned to work with big data? Share it with others in the comments.