3 Big Data Housekeeping Measures You Can No Longer Overlook

by   |   August 8, 2016 5:30 am   |   1 Comments

Dan Potter, Chief Marketing Officer, Datawatch

Dan Potter, Chief Marketing Officer, Datawatch

As the big data marketplace moves closer to a point of mass maturity, business leaders have begun to take new approaches to implementation and utilization. Advanced analytics solutions have made their way into a range of industries and regions, and companies that successfully align these investments with core goals and requirements will enjoy more progressive improvements to operational sustainability, intelligence, and general performance.

However, there is some housekeeping that must be addressed as organizations embark on big data and analytics initiatives.

Data preparation, information governance, and security are three fundamental elements of effective analytics strategies, and yet each has been largely ignored by many organizations in the rush to realize the promise of big data.

 

Data Preparation

Business leaders across industries now use big data analytics technology for a wide range of processes, objectives, and management needs. But while the technology is there, studies have shown that return on investment has been elusive at best for the vast majority of adopters. In fact, business analysts claim that 80 percent of their time is spent preparing data for analysis, and they still never seem to have the information they need.

Self-service data preparation is a critical, yet often overlooked, factor in the analytics process. Anyone can easily connect to relational data, CSV format, and other standard, structured data. But the data that provides the most analytical value often is locked away in multi-structured or unstructured documents, and it seems impossible to use this information without rekeying the data or asking IT for help. And with the volumes of data being created each day in various locations and formats, business users and data analysts don’t have time to wait for a specialist to create and run a report, or time to grapple with IT to gain only limited access to data repositories.

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Business users (aka non-IT experts) must be able to quickly and easily access all types of data – including multi-structured and unstructured sources such as PDFs, text reports, and Web pages, as well as real-time streaming data – across a variety of internal and external sources. Self-service data-preparation technology can enable users to extract, cleanse, prepare, and blend this otherwise unworkable data, transforming it into high-value information for solving business problems. Data experts, meanwhile, are liberated to spend the majority of their time on analysis instead of data preparation.

Information Governance

Many organizations today are also struggling to reconcile information governance with strong analytics performance. One of the biggest benefits of self-service analytics lies in the ability to rapidly combine and analyze data from a variety of sources. However, this can sometimes introduce serious governance challenges, given that half of this data typically comes from sources that aren’t managed by IT.

While most organizations have well-defined strategies for governing data that lives in managed systems, such as enterprise applications or data warehouses, analysts often need to pull data from non-managed sources, like CSV or text extracts from transactional systems, personal spreadsheets, third-party reports, or semi-structured content. Without proper governance, this can create big headaches around version control, data breaches, reconciliation, auditing, etc.

As big data becomes a more central aspect of corporate strategy, organizations must take important steps toward optimal information governance and then tailor their initiatives, policies, and strategies to adapt to the world of advanced analytics. When governance comes off the rails due to an advanced analytics project or any other reason, the chances of maintaining tight control over information and privacy while simultaneously enjoying high returns on big data investments will be inherently lower.

Security

For all of the benefits big data provides, many professionals – especially in IT – remain fearful about the security issues it poses. And you can’t blame them. IT departments are on the front lines, tasked with a never-ending battle to mitigate risks introduced by big data’s volume, velocity, and variety. Additionally, advanced analytics tools and responsibilities are still somewhat new in the context of modern business intelligence solutions, causing many firms to struggle when searching for the right balance between protection, privacy, transparency, and return on investment.

Introducing data-masking approaches as part of the data-preparation process is a great first step in protecting sensitive information. However, more work needs to be done to ensure that the deployment of wide-reaching big data programs is both profitable and positive, rather than representative of much greater risks to information integrity and security. The goal is to get more out of analytics investments without bolstering risk levels.

A New Era in Big Data and Analytics

The importance of security and information governance on big data and analytics implementations is clear, so why have many organizations failed to give them the attention they deserve? The answer is simple: they have historically impeded business processes and prevented business users and data analysts from doing their jobs effectively. But self-service data preparation has changed this and made data discovery and advanced analytics winning propositions for both end-users and IT.

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The age of self-service analytics dawned years ago, after data and sources became so locked down by IT that users lost their ability to access a wide breadth of data for visualization and analysis. To get immediate results, analysts began resorting to the sources that were generally available to them, namely Excel. And once they started showcasing the insights that could be developed from such a small amount of valuable data, the self-service analytics movement took off. Suddenly, the desire to leverage Excel data for immediate business value became far more important than taking the time to track which data sources were available, who was accessing them, and how information was being repurposed and changed to support analytics processes. It was the “Wild West” of the data world. No one knew where data was coming from or who was managing it. Information was floating around without auditing or classification. And security and governance were neglected because they slowed down analysts’ ability to do their jobs.

Today, there’s a new sheriff in town – self-service data preparation, which is now being recognized as the answer to the big data security and governance challenge and a necessary component of any data discovery or advanced analytics implementation. Self-service data preparation drastically reduces the time and effort that analysts spend on prep work and enables them to leverage the widest variety of sources while keeping these corporate assets protected.

In today’s big data and analytics landscape, business users can now be autonomous without causing disorder, and companies can leverage their intelligence investments while proactively mitigating threats. New approaches and technologies deliver the ease of use and agility that business users want, as well as the scalability, automation and control that IT demands. It’s time to address security, governance, and data preparation head-on. Companies can no longer afford to sweep these three housekeeping items under the rug.

Dan Potter is chief marketing officer at Datawatch Corporation. Previously, Dan led the product marketing and go-to-market strategy for IBM’s personal and workgroup analytics products and the online community and social media strategy for IBM’s AnalyticsZone.com initiative. Formerly, he held senior positions with Oracle, Progress, and Attunity, responsible for identifying and launching solutions across a variety of markets, including data analytics, cloud computing, real-time data streaming, federated data, and e-commerce.

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One Comment

  1. Posted August 8, 2016 at 11:19 am | Permalink

    Very interesting article Dan, I fully agree with you. I would add that the key to delivering this, is not an in house approach, but one based on true digital collaboration, between for example the enterprise, innovative technology companies (often start ups and SME’s) and other 3rd parties such as digital agencies.

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