Big Data + Analytics + Business Operations = Magic

Big Data + Analytics + Business Operations = Magic

The pace that companies generate and collect data shows no signs of slowing down.

These businesses are then pouting all of this data into data lakes in the hopes of eventually getting some business value out of it. But without a well-defined big data strategy, these data lakes, more often than not, turn into data swamps of stale, stagnant and useless data.

Unless businesses correlate downstream actions and results back in an integrated closed-loop, big data will never become smart data.

Data, analytics and business operations make up the three legs of modern business management. All three are interconnected. Each leg impacts the others and receives information from the others.

Analytics need reliable data and closed-loop feedback to produce relevant insights. Operations need relevant insights and accurate master data profiles for decision management. In turn, data management requires data quality insights from analytics and current operational data.

To develop a sturdy three-legged stool of big data, analytics and operations, that helps establish a data-driven decision management, companies need a closed loop data management model. Here is a five-step closed-loop model to get maximum return from big data initiatives.

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For analytics to provide accurate and relevant insights all master, transactional and interaction data needs to come together. Collect data from all sources: internal, external, third-party and social and blend them into one consolidated data foundation.

Create accurate master data profiles for business entities (customer, account, product, employee, location) and share across analytics and operational applications. Utilize rules-based modern data management capabilities like a match, merge, verify and de-duplicate to create a reliable data foundation.

Once a reliable data foundation is in place, the analytics will be more meaningful. Graph technology can reveal insights into your data and relationships between the data entities like people, products, accounts and locations. Machine learning and predictive analytics provide next-best-action recommendations to business users.

Use analytics not only to monitor business performance metrics but to analyze the quality of the data itself.;

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