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Analytics Strategies | @CloudExpo #BigData #Analytics #IoT #M2M #CIO
New approaches and technologies deliver the ease-of-use and agility that business users want
By: Dan Potter
Jan. 30, 2017 12:30 AM
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
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
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.
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 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
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