Taking Control and Harnessing the Power of Data with Machine Learning

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In this special guest feature, Rob Perry, VP of Product Marketing at ASG Technologies takes a look at how machine learning can help organizations better understand their data and automate complex processes to derive more value from the data while still staying compliant. Rob has a broad background in product management and product marketing developed at a range of technology industry leaders including CSC, Inc, Siemens, Microsoft and Lotus Development. He’s been part of teams bringing major software products to market including SharePoint 2007, Lotus Notes and Lotus 1-2-3. In addition, Rob was an analyst covering the market for Internet information tools at Yankee Group. He has a BA in Economics from the University of Virginia and lives south of Boston where he sails in the summer and skis in the winter.

Organizations today collect more data than ever before. From individuals’ online shopping habits to social media activity – Facebook collects more than 500 terabytes of data per day – organizations have massive amounts of information about existing and prospective customers at – or perhaps just beyond –  their fingertips. While this presents an opportunity to learn more about customer preferences and enhance the overall customer experience, much of this data goes underutilized or simply unused. More often than not, organizations are stymied by the sheer volume and complexity of the data available and have difficulty understanding the numerous rules and regulations governing what they are able to do with it.

As machine learning and AI approaches have matured, many organizations have begun looking to solutions that use these technologies to help automatically and intelligently comb through, make sense of and glean insights from their data. Here is a look at how machine learning can help organizations better understand their data and automate complex processes to derive more value from the data while still staying compliant.

Understanding all that machine learning can do

For years, IT teams have been tasked with ensuring data is being used correctly and to its full potential, which has meant involving complex algorithms, ETL processes and hours of manpower. Today’s big data technologies, which ingest and store massive volumes of data, exacerbate the challenge of turning data into actionable insights.

Whether it’s too much complexity, a lack of understanding of what exactly is on hand, or an inability to make sense of the data for decision making, organizations are being slowed down by information overload. On top of this, depending on the industry, organizations have very different uses for their data and different regulations governing that use.

This is where machine learning comes in. Machine learning solutions can recognize and learn from patterns in data and apply statistical analysis to predict outcomes and make recommendations, getting better at it as it processes more data and gains more knowledge. By doing so, machine learning can take on the complexity around data analysis and can provide organizations with a better understanding of their data while allowing them to use it more efficiently and effectively. Whether it’s simple categorization and filtering, or the ability to recognize patterns, draw correlations and find related data sets, machine learning and other analysis tools provide organizations with both a holistic view of their entire data lake and a more granular view of the relationships between these pieces of information.

These capabilities improve over time as the solution becomes more sophisticated and can draw stronger conclusions around, and correlations between, data. For organizations hindered by massive amounts of information, these tools can be invaluable, allowing organizations to do more with their data than they thought possible. For instance, they might leverage input from crowd sourcing, detect patterns in data, discover metadata, uncover pattern in queries or reveal gaps in information.

Additionally, machine learning can improve the quality of automated data tagging – allowing organizations to categorize specific data sets with better results, and then recommend data items to specific users. This is particularly helpful for ensuring organizations stay compliant as data-oriented regulations – like GDPR – take effect by identifying personal data subject to the regulation in the vast data resources being managed. Highly regulated industries – such as insurance, healthcare and banking – already know the struggle of ensuring data is being properly gathered, stored and documented, but as GDPR looms closer, organizations across all sectors will be forced to shoulder the burden as well.

Machine learning adds value to this process, with its ability to automatically recognize and classify Personally Identifiable Information (PII). It does this by leveraging metadata and ensuring it remains compliant by incorporating data lineage and analysis, and anonymizing and pseudonymizing this data when necessary. It is also able to detect anomalies, such as encrypted data that is traveling through an organization unencrypted.

As organizations continue to gather data about their customers and other parts of their business, they will increasingly look for solutions to help them turn that data into valuable and actionable insights that drive competitive advantage. As they look to invest in these solutions, it will be important for them to consider their specific needs – everything from scalability and usability to integration and customization options. Once they find the best solution for them, everything from data management to compliance will become easier, taking up less of the organization’s time and allowing them to function more efficiently and effectively.


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