Big Data for All

by Sri Raghavan why-teradata

Organizations understand the value of big data analytics and discovery, which is why they continue to invest in data scientists and business analysts who can generate incredible insights. Unfortunately, barriers still exist that keep analytics and discovery models from being consumable by the masses.

Business users desperately want to utilize discovery and big data insights, but to do so, they require an easy-to-use, easy-to-access, interactive visual interface to leverage the information. Typically, big data platforms that have any market presence today are not accessible to the vast majority of business stakeholders because there is no easy way to deploy advanced analytics models.

Another obstacle is that the platforms do not make the models easily repeatable, which would allow users to focus on operationalizing the insights rather than figuring out how to conduct the analytics. Organizations are therefore forced to invest in hard-to-obtain and expensive resources to consistently staff their analytics initiatives. Over time, this strategy becomes difficult to sustain.

A solution is needed to overcome these traditional challenges. It needs to extend and enable the value of discovery analytics to a wider business user and BI community that goes beyond SQL-savvy analysts and data scientists.

Deliver Value to a Larger Community

With the adoption of big data apps, a user base can grow and make analytics easier for more people. SQL users of discovery platforms can employ apps to capture innovative analytic logic, then deploy and share the information with a broad group across the enterprise. Once big data analytics and insights are in business users’ sights, the number of users and workloads will increase—and so could the ROI.

Two distinct groups will significantly benefit:

  • When the platform provides a graphical user interface (GUI) and standards-based app building and configuration, IT personnel can leverage them to allow users such as data scientists, developers and analysts to seamlessly and quickly build, configure, deploy and share big data apps.
  • The business rank and file, from the C-suite to the line of business managers and analysts, will need to utilize an interactive, Web-based user experience. This will allow business people to analyze, view and share results from big data apps and focus quickly on operationalizing the insights to encourage innovation across the entire organization.

The solution also needs to be compatible with BI tools such as Tableau, Microstrategy and others. The underlying discovery platform should provide a REST API to allow BI and visualization tools to call the apps for easy integration with these tools and other open-source visualization packages to extend the capabilities of the solution. In addition, the visualizations should include Sankey, Chord, Sigma or a similar offering to execute the pre-built analytics functions. However, they are not intended to replace the visualization capabilities of the BI tools.

The Power of Big Data Apps

Big data apps are scalable, reusable, industry-focused applications that complement discovery platforms to address specific business questions for organizations across all industries. The apps target particular business challenges such as fraud, customer churn and loyalty. They can also be used for process optimization, purchase paths and cart abandonment, patient treatment paths, influencer behavior, call center optimization, review analysis and other important use cases.

The exploitation of big data apps will significantly enhance any organization’s ability to improve its culture of data-driven decision making. This happens by enabling all stakeholders to share in the deployment, consumption and operationalization of analytics. The result is a substantial reduction in the IT department’s burden of maintaining and managing complex solutions. At the same time, the apps are not hindered by traditional technology barriers, so they can increase the ability of the business to proactively address crucial challenges on its way to greater innovation, reduced overall expenditure and higher profitability.  

Read the full article and more in the Q2 2015 issue of Teradata Magazine.

Sri Raghavan is a senior product marketing manager at Teradata. He has more than 18 years of experience in advanced analytics.





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