Data – and the ability to capture it, analyze it, and take action based on it – may be the single biggest differentiator between the highest performing companies and everyone else.
This according to the IBM Research publication, "Leading Through Connections," the findings from face-to-face interviews with over 1,700 CEO's from around the globe.
Successful businesses are successful at turning data into action. The top priorities of CEO’s in the highest performing companies:
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.
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. 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 By the time one big data project is completed, it is obsolete - lost under another avalanche of big data.
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 Beneath all of the data is ROT - data that is redundant, outdated or trivial.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 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 addressing data ROT is put off too long, many businesses will find that it is too late to catch up.
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focus on the little data issues, not only is ROT reduced, but big data growth is slowed (by eliminating redundant and outdated information), which helps to reduce the overall problem.
There are four priorities that the leaders focus on to extract the greatest value out of big data investments, 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. That’s one day each week. Take your annual overhead, multiply it by 20%, that’s how much your organization spends on looking for stuff.
Oh, and by the way, all that frustration spent looking for stuff – it’s decaying employee morale.
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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