As businesses realize the importance of big data, adoption is on the rise. A recent survey from NewVantage Partners found that 62.5% of firms now have at least one big data project in production.
But big data is still not in full-scale production everywhere, and many companies say operational challenges hold them back. A report by IT industry association CompTIA revealed that less than one-third (31%) of companies are exactly where they want to be in managing data. Furthermore, almost half (45%) said that a high degree of their data is fragmented.
So what are the operational challenges that big data presents? We are seeing a confluence of three key features of big data.
1) Volume. More data was created in a 2-year period than in the entire previous history of the human race according to SINTEF; 2) Variety. The proliferation of mobile devices, sensors and connected things means businesses are dealing with different types of data; 3) Velocity. The data generated needs to be processed rapidly to glean “on-time” insights.
This data requires new methods of collection, storage, organization, and analysis—a process that many companies struggle with operationalizing. To overcome operational challenges, business leaders need to implement automation processes and strategies that help data teams access the right data at the right time to extract the right insights.
Successfully Operationalizing Big Data Programs
To leverage the benefits of the data explosion, organizations need to prepare their IT teams properly. An IT operations management tool with enterprise-grade capacity optimization and visualization can help IT plan and right size the data ecosystems—including compute, storage, and network resources, ensuring control over infrastructure costs.
When data is siloed, such as residing in self-contained departmental databases, you’re going to get lackluster insights. It is important to take a holistic approach to a big data strategy. The big data technology ecosystem must interface with enterprise applications and data sources, such as ERP solutions and connected devices, to integrate data in one central location.
Right from the pilot phase of a big data initiative, automation processes must be put in place to ensure that data from across multiple sources can be seamlessly ingested. Automation is faster and more accurate than manual methods.
Some big data use cases will likely cross organizational boundaries, so another consideration, or often concern, is how data stays safe at each step. You can’t protect what you can’t see. Ensuring holistic visibility of how the big data infrastructure is connected to enterprise applications enables businesses to take security measures—from managing access based on the level of data confidentiality within the company to classifying which customer data the company has permission to mine.
Big data success relies on a mind shift too. Organizations should address cultural changes, such as getting employees committed to keeping databases refreshed with relevant information by set times, so data teams always work with up-to-date data.
Extracting Value From the Right Data
Organizations can use big data analytics for multiple use cases reaching across the business—from understanding customers to drive engagement, retention, and sales, to analyzing internal processes to streamline operations.
But the most valuable use of big data analytics is not reporting what’s already happened; it is accurately predicting future outcomes and behavior affecting the important areas of the business. Through predictive big data analytics, organizations can change operations in real time and build strategic, forward-looking plans to drive faster business outcomes, which give companies a competitive edge.
Data will be the difference between companies that propel forward and those that stay behind in the digital era. Although data can oftentimes feel like an uncontrollable force, it can be managed. Don’t let operational big data challenges derail your mission for insights. Invest in automation and getting the right strategies in place—both technological and cultural. We all know you can’t run before you can walk. So before you can perform accurate analytics, the operational foundations must be laid. It’s a key step in the recipe for big data initiatives to succeed.
Robin Purohit is group president of the enterprise solutions organization at BMC.