The pace that companies generate and collect data shows no signs of slowing down.
These businesses are then pouring 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.
The 3 Legs of Modern Business Management
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.
5 Steps to a Closed-Loop Model
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. Keeping good data hygiene is an ongoing process.
Create data storyboards that provide visibility into the state of your business. Network graphs can help you understand the complex web of relationships between business entities such as customers, accounts and products. Visual graph technology running on top of a big data store will uncover the affiliations as well as the influence of your clients in the network. Visualization of customer profiles — complete with interactions, transactions and analytics insights — helps create the true 360 view for better customer understanding.
Offer the insights and graphical visualizations within operational applications, rather than isolating them in separate reporting or analytics tools.
Great visuals and guided recommendations from analytics, when offered inside applications (CRM, ERP, HRM) in the context of the employee's role and objectives, help frontline employees make informed decisions. For instance, insights like customer value, churn propensity and buying frequency in contact center applications help agents to serve customers better. Recommendations from machine learning guide agents to offer the right product to the right customer, at the right time.
Now your big data efforts are delivering demonstrable ROI. The applications can be Customer 360, account management, market segmentation, campaign management or any other type of data-driven application.
Close the Loop
To keep the gains coming in, capture feedback from applications and users to improve the data quality and usefulness of analytics. Data quality and reliability are paramount in obtaining actionable insights from big data to improve business efficiency and customer experience.
Modern data management technology can help consolidate and cleanse data from all sources, transform it into reliable data, and provides relevant insights and recommended actions in the context of your operational applications.
With proper data management methods in place, companies are not only growing their business and keeping their competitive edge but also creating new revenue streams with data monetization. All of this starts with improving the reliability and relevance of your internal data to improve your business operations. From there you can turn your data into an asset.
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