8 Big Data Turkeys and How to Keep Them From Giving You Heartburn

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8 Big Data Turkeys and How to Keep Them From Giving You Heartburn

Data is only good if it is reliable, accurate and timely. When data is poorly handled, it can produce a number of issues that could cause any IT exec to stress.

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IT Doesn't Know How to Properly Support Real-Time Analysis of Data

According to a recent study from Vanson Bourne, commissioned by Software AG, 73 percent of organizations find analyzing data a major challenge. Most organizations can't act upon data in real time, causing them to miss out on revenue opportunities and struggle to predict future trends. To offer better services, improve procedures and make smarter strategic decisions, organizations must learn to properly support real-time analysis of data. When data is successfully used to improve processes, it can create both competitive advantages and opportunities for an organization.

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Organizations Lack Technology to Best Acquire Data

Many organizations are not equipped with the right technology and processes to receive and analyze data effectively. Slow and manual processes have led to an overall lack of confidence in the data that decision-makers have at their disposal. Choosing the technology that will be used is only half the battle when preparing for a big data initiative. Organizations must have the right technology and analytics tools in place to successfully manage a big data initiative. In addition to the initial development work, organizations must have a maintenance plan in place to continually make updates to the existing tools and technology.

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Not Enough Data Is Collected for Effective Analysis 

Organizations with insufficient data struggle to draw effective and accurate conclusions from their data. Data sets need to be robust and organized in a way that provides meaningful insights to various teams. To be successful with big data, organizations must have enough data collected to analyze. However, many organizations struggle in finding the right balance. When is enough data enough?

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Time Suck and Disruptive Drain for IT

According to the study, 87 percent of respondents said it's important to make operational decisions swiftly, but 85 percent still experience issues in attempting to do so because they can't use their data quickly enough. Real-time data analytics can be time-intensive and hinder staff productivity. IT and data-management teams are already stretched thin to maintain their daily operations, deliver reports and analyses, and incorporate new procedures. Stretching employees across too many roles or short-staffing the day-to-day work can be very disruptive to the IT team. In fact, many successful practitioners report that their big data teams successfully operate separately from other data management teams.

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Lack of Secure Data

Many companies fail in using big data because they lack a proper balance between value and risk. Data security can present big challenges for companies, especially those that house sensitive information. Organizations must understand the risks involved in big data, and security measures to prevent data breaches and unauthorized access from taking place must be thought of and planned for in advance. Big data breaches have the potential for very serious consequences and can be harmful to a brand's reputation and produce legal repercussions, especially in those markets with highly sensitive and secure information.

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Failing to Capture Data That's Useful

According to the survey, 73 percent of organizations reported that analyzing a lot of data is a major challenge for them. Organizations often make the mistake of analyzing data without considering other data points that might be crucial for the analysis. Some companies report they lack the ability to effectively collect and analyze information, and as a result are only able to draw basic (and not meaningful) conclusions from their data. This is a scary outcome for those in charge of presenting findings to senior staff. When dealing with big data, companies must be strategic. One of the first steps that organizations need to take is to decide what they want to get out of data—i.e., goals and priorities.

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Lack of End-to-End Data Governance Process

More than half (56 percent) of respondents reported they are using manual processes to collect and analyze data. These types of processes are slow, not comprehensive and have a high chance of human error. Automated solutions provide a better alternative for big data analytics, if the right system is put in place. However, having a poorly configured or substandard automated solution can produce frightful outcomes. Only successfully implemented solutions that collect and analyze data automatically will help decision-makers feel more confident in the data that they use to prioritize processes. With the right end-to-end data-governance process in place, companies will produce more reliable results from their data.

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Data Overload

As today's organizations are increasing their investment in big data, there's been an increased volume of data coming in. Survey respondents reported that the volume of data that their organizations collect and analyze increased by 12 percent within the last year alone. This means that organizations have had to continuously react to new data coming in and try to sort through what is useful or not. Despite increased investment, organizations need to keep reacting and investing in data as decision-makers expect the volume of data will further increase by 19 percent in the near future.

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7 Companies Streaming Real-Time Data to Drive Innovation

Now that big data is a reality for businesses of every size, companies must seek technologies that bring data into view in real time to be competitive. New technologies—including Apache Kafka, Apache Spark, in-memory databases and in-memory file systems—are innovative approaches to streaming real-time data that deliver relevant information and drive the creation of new services. The following use cases demonstrate how powerful a business transformation can be and how quickly companies can shift when immediate access to all new data sources, types and streams are viewed with a collective lens. This eWEEK slide show taps online archives, the Apache Kafka and Apache Spark Websites, and interviews with officials at in-memory database company MemSQL to highlight modern approaches to streaming real-time data from companies that are breaking new ground and pushing the boundaries of what is possible...
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