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New approaches and technologies deliver the ease-of-use and agility that business users want

Three "Housekeeping" Trends Impacting Big Data and Analytics Strategies

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, yet, ironically, each has been largely ignored by many organizations in the rush to realize the promise of Big Data.

1. Data Preparation
Business intelligence and Big Data analytics tools have fundamentally transformed the way organizations operate. 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 percent1 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 and other standard, structured data. But often the data that provides the most analytical value 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 to grapple with IT to gain only limited access to data repositories.

Business users (AKA non-IT experts) must be able to quickly and easily access all types of data - including information from 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 play a pivotal role in this endeavor by enabling users to extract, cleanse, prepare and blend this otherwise unworkable data, transforming it into high-value information for solving business problems. Data experts are liberated to spend the majority of their time on analysis instead of data preparation.

2. 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, like 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.

3. 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.

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. So much so, that the desire to leverage Excel data for immediate business value became far more important than taking the time to track what 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 not only drastically reduces the time and effort analysts spend on prep work, but it 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 best 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 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.

1. Forrester Blog, 3 Ways Data Preparation Tools Help You Get Ahead Of Big Data, February 2015: http://ibm.biz/Bd4DJm

About Dan Potter
Dan Potter is chief marketing officer at Datawatch Corporation. Prior to Datawatch, he held senior roles at IBM, Oracle, Progress and Attunity where he was 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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