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Don't Get Tripped Up By These Big Data Problems Featured

Don't Get Tripped Up By These Big Data Problems Sharon McCutcheon

Before going into any battle, generals must ensure that they have the necessary knowledge about their enemy. The knowledge is on the size of the army, weapons, and the tactics they use in their fights. With this knowledge, the right strategy can be crafted to outwit the enemy. Just like the generals, every decision-maker has to have background information before arriving at specific decisions that will impact the operations of their businesses. While at it, it is crucial to understand the big data problems. Here are some big data problems that you should not allow to trip you up in your operations.

  1. Integrating disparate sources of data

Variety, which is associated with big data, causes immense challenges in data integration. This is because big data comes from different places such as enterprise applications, documents created by employees, social media platforms, and email systems, among others. As such, combining all the data from these sources and reconciling it to create informed reports can be incredibly difficult. While vendors offer different data integration tools which are designed to ease the process, the data integration problem persists. Companies must, therefore invest in new big data tools that can merge disparate data sources to create insights that can be used to make critical business decisions.

  1. Getting insights from massive data

Varieties of data sources and technologies that are now available can be confusing and may get one lost in the market—as such, getting insights from these large lumps of data is not always easy. It is easy to gather data but making sense out of this data is not always as easy as some people might think. Many organizations often fail to measure the right variables since they cannot detect the correct data from massive data that exists. It is therefore essential to find a  discernible signal in noise that can be assessed and used to make critical business decisions. This can only be achieved by using scientific approaches to the data.

  1. Fast technological changes

Bigger companies are more likely to be affected by data silos because they like keeping their data on-premise and are also slow in adopting new technologies. This often leaves them with a challenge of intense competition from smaller companies that are always faster when it comes to the adoption of new technologies. According to a report by Capgemini, companies like telcos and utilities often notice high levels of disruption from new competitors. From this report, it is clear that traditional players are slow to adopt new yet essential technologies such as big data. As such, failure to obtain all the relevant data, analyze it, get actionable insights, and use them in operations can affect events.

  1. Recruiting and retaining talent

For any organization to develop, manage, and run applications that generate insights, there is a need to top professionals with exceptional big data skills. This has seen the demand for big data experts going up with time. Salaries have also increased dramatically as a result. A 2017 report by Robert Half Technology Salary Guide indicated that big data engineers earned between $135,000 and $196,000 on average. On the other hand, a data scientist earned salaries ranging from $116,000 to $163,500. This is the biggest challenge for many organizations, as big data increasingly become highly in demand.

  1. Inaccurate data

Data silos are ineffective on an operational level and are also a good breeding ground for the inaccuracy of data. Experian Data Quality in its report indicated that about 75% of businesses believe that customer contact information is incorrect. In such a scenario, inaccurate customer data might mean that there is no data at all. The best way to correct this is by eliminating data silos through data integration.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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