How can your business achieve agility with big data?
As technology advances at great speed, our lives are becoming increasingly digitized. From Twitter feeds to sensor data from medical devices, companies are overwhelmed with big data yet starving for actionable information. Most likely, you’ve heard a lot of talk about the volume, variety, and velocity of big data and how challenging it is to keep up with that explosion of data.
For many enterprises, their ability to collect data has surpassed their ability to organize it quickly enough for analysis and action. Executives, IT staff, and analysts alike have been frustrated with the traditional rigid processes for data processing that require a series of steps before data is ready for analysis. Relational databases and data warehouses have served businesses well for collecting and normalizing other relational data from point of sale (POS), ERP, CRM, and other data sources where the data format and structure is known and doesn’t change frequently. However, the relational model and process for defining schema in advance cannot keep pace with the rapidly evolving variety and format of data.
Basically, all types of businesses are jumping to big data, but some are having much better results than others. So, where do some enterprises go wrong, and where do others go right?
- Achieving good results with big data starts with sufficient system capacity. When leaders engineer the right kinds of solutions for a big data environment, the hardware can easily process its workloads, and people don’t have to run around trying to solve network capacity problems. This means allocating enough CPU cores or processing power to central servers, addressing needs for dynamic memory, and providing adequate storage solutions, along with monitoring how data will flow through the system and identifying and eliminating any traffic jams.
- Another big part of agile big data has to do with people. A business has to have the right training and the right resources for implementation. Having adequate talent on board is vital, and where there are any gaps, quick and effective training and cultivation of in-house people is essential. Companies can rely on consultants for many things, but at the end of the day, there needs to be enough savvy about these big data systems for the business to handle them confidently.
- Another fundamental area of using big data correctly comes in when businesses start to actually use the data they’ve collected. Adequate hardware systems can perform data operations well, and talented people can maintain and use them correctly, but there’s a big difference in the results that companies get, based on how the system builds reports, selects data, and presents the right results in the right ways. This has to do with sorting through structured and unstructured data sets conceptually, not going into the system and head-counting data, but instead, having a philosophy of data that focuses on just the most vital data sets and discards irrelevant and unusable data.
All of these strategies will lead an enterprise to eventual success with big data systems. It’s not about how much data you can store and process. Instead, it’s about data agility, meaning how fast you can extract value from your mountains of data and how quickly can you translate that information into action?
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