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Biggest Problem with Big Data: Search… or Find?

Written by Noah Bethilda

With the continuing exponential rise of data volumes one of the biggest problems that doesn’t get an airing is the continuing challenge, not of munging, prepping, and analysing, but a far more basic one – searching, and then finding.

Never mind ‘you can’t manage what you can’t measure’ – if a business can’t find the right data in the time available, it will use the data that it can find – and that may lead to outcomes ranging from less precise decisions to out and out poor decision-making.

According to a landmark 2012 McKinsey research study showed that 19% of the average work week is spent unproductively searching and gathering information. McKinsey found that social technologies, which create value by improving productivity could potentially contribute $900 billion to $1.3 trillion in annual value across four sectors they reviewed: Consumer packaged goods, retail financial services, advanced manufacturing, and professional services. McKinsey estimated that the average interaction worker spends 28 percent of the workweek managing e-mail and nearly 20 percent looking for internal information or tracking down colleagues who can help with specific tasks.

A statistic by Outsell showed that an engineer’s time spent searching for information has increased 13 percent since 2002.

And a survey by SearchYourCloud revealed that workers can take up to eight searches to find the right document and information they need.

One final stat: An IDC research paper has shown that “the knowledge worker spends about 2.5 hours per day, or roughly 30% of the workday, searching for information….60% [of company executives] felt that time constraints and lack of understanding of how to find information were preventing their employees from finding the information they needed.”

There are a multitude of such studies repeated over the years. It all goes to show that data is unambiguously a respected resource, but that the search and find aspect of managing it has become a troublesome part of the knowledge worker’s role.

Part of the problem is purely an issue of volume. Many organisations keep so much data that of course sifting through it becomes a challenge. And yet there are simple ways to help alleviate the chore that finding the right data has become in the minds of many business analysts, simply trying to find the data they need to get their jobs done.

Taking inspiration from social tools, there’s no reason that means that analysts have to reinvent the wheel, and investigate the same data sets every time in their search. In the same way that tools like Slack and Asana have revolutionised the way distributed teams can collaborate, there are ways the world of data can draw on similar functionality to help them rate the reliability and utility of the data at hand.

In the same way that popular posts can be uprated on Reddit, liked on Facebook, or commented on in Asana, valuable data sets, trusted and clean numbers can be preserved and assigned trust ratings. That way the business preserves knowledge and speeds the process of getting to the right answers faster, time after time.

Most analysts spend more time searching for information that may be relevant to their analysis than on the analysis itself

This is important. Too much time spent on the front end translates to inefficiencies and delays in answering critical business questions and, in turn, implementing the actions that can improve the bottom line. And often, others in the organisation may have already collected the same information or performed a similar analysis, but colleagues have no good way of finding it. In many cases, if they can’t find it, they re-create it. This is obviously an inefficient use of time and effort. Data assets and resulting information proliferate, thus compounding the problem and creating inefficiencies and delays in answering critical business questions. It’s a pickle without the right collaborative solutions – and the collegiate culture needed to make them work.

Ideally, any new functionality should be aimed at changing how business analysts discover, prioritise, and analyse all the relevant information in their organisation. Not to get too far from measurable metrics, but it should simply bring back the fun of allowing people to do their jobs better. There is a thrill to the analytic experience where data and human insight interact. But without the ability to easily get to and enjoy the most challenging and rewarding parts of the job, analysts may feel less empowered, and less engaged with the work, and lose the ‘flow state’ we all strive to achieve.

Best practices for searching, and finding, what you need

Although organisations are generating, storing, and accessing more data than ever before, this data is often hidden to the people who need it to make critical business decisions. Sometimes the data conflicts with what people think they know, calling its trustworthiness into question. It’s important to have some clear guidance.

Investigate everything in the analytic process. Search to find and reuse information contained across analytic applications, workflows, macros, visualisations, dashboards, and data science models. Resources should be returned to where they have proven their value.

In a pinch, only use trustworthy, vetted, and curated information. By doing so analysts reduce the time spent searching for impactful data and assets, and avoid ones that have been rated poorly by others who have been there before.

Utilise your organisation’s tribal knowledge. Discover the types of information your data contains, where the information comes from, who is using it. This may be most easily done via a data platform, though it’s possible to curate such resources by hand – if you have the time, and a limited, slow moving data environment. This is the difference between a long search and a quick ‘find’. A ‘business glossary’ – where you establish business definitions, terms, rules, or processes to ensure data governance and consistency in data and analysis – may prove a great way to make this happen in a controlled and useful way.

For the larger and more complex organisations, guidance is important, but even more important is the culture. But over a certain size even best practices and the best cultures won’t keep up with the volume, velocity and variety of data. That’s when the platform becomes paramount.

But make no mistake, any size of business can lose time with inefficient data search and find. We can all use the extra time gained by making better decisions faster to drive more positive change for our organisations.

The post Biggest Problem with Big Data: Search… or Find? appeared first on Computer Business Review.


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