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Business and IT Alignment Key for Data Analytics Success

Business and IT Alignment Key for Data Analytics Success

by DAVID WELDON APR 18, 2016 6:58am ET

Originally published at http://www.information-management.com/news/big-data-analytics/business-and-it-alignment-key-for-data-analytics-success-10028651-1.html

Investments in data analytics are often driven by business users, and then handed off to IT to manage. But it requires a collaborative effort on both parts to achieve full success with data analytics initiatives.

Information Management recently spoke with Ajay Khanna, vice president at Reltio, a developer of data driven applications, about what his company is hearing from customers this year, and what attendees at the recent Strata & Hadoop World conference most had on their minds.

Information Management: What are the most common themes that you heard from conference attendees?

Ajay Khanna: Two most common themes during our discussions with participant included:

First, graph technology and Spark analytics. This was one of the topics that triggered a lot of excitement. Many participants were exploring new ways to understand and visualize the relationships across the organization, creating graphs for customers, products, employees, and places.

There were also in-depth discussions on how to use Spark analytics to determine the influence of an entity in a graph, measure strength of relationships, entity resolutions in the graph, and solve complex business problems like house holding.

The second very frequent discussion was how to demonstrate the business value of investments in data lakes. Participants who had already invested were finding it hard to leverage data lakes for any relevant analysis due to data quality issues.

IM: What are the most common data management challenges that attendees were facing?

AK: Many participants we spoke to discussed using Hadoop to create data lakes but were struggling to justify business value of their investment.

Another common challenge that we came across was the data reliability issue. Companies are still struggling to blend master data profiles with transactional and big data to run analytics and make data-driven business decisions.

Related issues that came up were how to easily make all the data available to Spark environments for performing analytics. It takes too much time to prepare data for any reasonable analytics.

Another challenge discussed was the absence of a closed-loop between Spark analytics and business operations.

IM: What are the most surprising things that you heard from attendees regarding their data management initiatives?

AK: It was surprising to see so many companies investing in, or considering investment in data lakes without clear business objectives in mind. There seemed to be a disconnect between business strategy and big data initiatives.

IM: How do these themes and challenges relate to your company's market strategy this year?

AK: These themes and challenges are well aligned with Reltio’s mission.

Our mission is to help business and IT teams be right faster. Reltio Cloud’s modern data management Platform as a Service provides deep data management capabilities to blend data from all internal and external sources, and keep it clean. It includes graph technology to uncover and manage relationships at big data scale, augmented with predictive analytics and machine learning to offer relevant insights to business and IT.

Such modern data management foundation enables enterprises to build business oriented data-driven applications with reliable data, relevant big data insights and intelligent recommend actions. Our recent release Reltio Cloud 2016.1 addresses many issues we discussed with conference participants, including time to value with Spark.

IM: What do you view as the top data issues or challenges in 2016?

AK: The top challenge will be business and IT alignment. Investments in data lakes or big data analytics solutions will not be fruitful unless enterprises bridge the gap between analytics and operational needs.

Organizations need is to focus on developing a reliable data foundation that blends data from all sources and helps keep data clean, current, and complete. Business, IT, and data professionals must work together to bring the insights from big data analytics into business applications used by frontline users to close the loop.