Top 5 problems with big data – and how to solve them

Big Data

Top 5 problems with big data – and how to solve them

Pioneers are discovering all sorts of creative ways to utilize massive data to their benefit. Insights gathered from big data can result in solutions to stop credit card fraud, anticipate and intervene in hardware failures, reroute traffic to prevent congestion, guide consumer spending through real-time interactions and software, plus even more.

The advantages of large information are felt by companies also. 61 percent of organizations state that large data is driving earnings since it can deliver deep insights into customer behavior. For many companies, this view of the present data means obtaining a 360-degree perspective of the clients.

Thus, for many businesses, the largest difficulty is figuring out how to get value from this information. Just 27 percent of the executives surveyed in the CapGemini report clarified their big data initiatives as powerful.

This implies that there is a massive gap between the theoretical understanding of big data and really putting this theory into practice.

So what’s the issue?

Finding the signal in the noise

It is difficult to acquire insights from a huge lump of information. Maksim Tsvetovat, a large data scientist at Intellectsoft and writer of the publication Social Network Analysis for Startups, said that to use big data properly, “There needs to be a discernible signal in the noise which you’re able to detect, and sometimes there just isn’t one.

“Once we have completed our wisdom on the information, occasionally we have to come back and say we just didn’t quantify this right or quantified the wrong variables since there’s nothing we could detect here”

Also read: The ‘Failure’ Of Big Data

So among the biggest problems faced by companies when managing big data is a traditional needle-in-a-haystack issue. Tsvetovat went on to state that in its raw form, large data looks like a hairball, and a scientific approach to this information is essential.

Data silos

Data silos are essentially large data’s kryptonite. What they really do is store all that terrific data you have recorded in separate, disparate units, which have nothing to do with one another, and consequently, no insights can be gathered from the information since it simply is not integrated.

Data silos would be the reason you must crunch numbers to produce a monthly sales report. They are why C-level conclusions are made at a snail’s pace. They are why your sales and marketing teams simply don’t get along. They are the reason that your customers are looking elsewhere to take their company because they do not feel their needs are being fulfilled, and a smaller, more nimble business is offering something better.

And the best way to eliminate data silos? It is easy: integrate your data.

Inaccurate data

Not only are data silos ineffective on an operational level, but they’re also a fertile breeding ground for the biggest data difficulty: incorrect data.

Based on a report from Experian Data Quality, 75% of companies believe their client contact info is incorrect. If you have got a database filled with incorrect customer information, you might as well have no information at all. The best way to fight inaccurate data? Eliminating data silos by incorporating your own data.

Technology moves too quickly

Bigger businesses are more likely to fall prey to data silos, for such reasons as they would rather maintain their databases on-premises, and since decision making about new technologies can be slow.

1 example mentioned in the CapGemini report is that stalwarts such as telcos and utilities”. . .are noticing elevated levels of disturbance from new rivals moving from different sectors. This issue was cited by over 35 percent of respondents in all those businesses, in comparison to an overall average of below 25%”

Also read: What’s Inside The Big Data Toolbox

In nature, traditional players are far slower to embrace technological advances and are finding themselves confronted with serious competition from smaller businesses because of this.

Big information is also fast data. Paul Maritz, critical chief executive officer of the EMC Federation, wrote,

The ability to make quick decisions and quickly act on insights gained on large information is an edge SMEs have over big corporations.

Deficiency of skilled workers

CapGemini’s report found that 37 percent of organizations have trouble locating skilled data analysts to take advantage of their data. Their best bet is to create a single common data analysis team for your company, either via re-skilling your current workers or recruiting new employees specialized in big data.

You want to find employees that does not just know data from a scientific perspective, but who also know the company and its customers, and the way their information findings apply directly to them.

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