Here are five ways that companies are crunching the numbers and actually applying them to some concrete outcomes.
Port Big Data Directly Into Sector-Specific PlatformsOne easy way to start using aggregated business data is to put specific data elements into pre-designed business process systems that are made to deliver that data effectively. Perhaps the best example is customer relationship management (CRM) tools. Vendors often build their services around dashboards that can present sales workers and others with efficient and actionable customer files or folders.
The thing is that using CRM assumes that you have the necessary data somewhere. If you can group customer identifiers, purchase histories and other relevant items together, you can start shipping all of this into your CRM platform. Your sales team will thank you.
Build Out Legacy Business Intelligence SystemsAgain, you'll be picking and choosing what specific data sets you want to use, but another thing that companies are doing is taking their normal ways of crunching data and expanding them slowly, by injecting more and more sets of big data into their traditional reporting techniques.
OK, so there are more than a few cautionary resources out there about how much legacy systems generally hold back actual progress. But there are also some practical guides out there that show some of the challenges in using legacy technologies for big data, how it can be done, and how the right staff can make all the difference. Plus, technically, everything is "legacy" once it's deployed, so it doesn't always make sense to scrap a legacy system every time something better comes along.
Use that Data WarehouseIf you have big data in a central repository and you know how to access it, you can build new processes around that.
Here is an excellent example of how some larger companies are pursuing specific, precise, pinpointed uses of big data. You might call it cross-indexing; it helps an enterprise to construct consistent models between all of their numerous kinds of customer accounts that may be held in different parts of the software architecture.
By combining all actionable data together, a company may be able to see if, for example, a name in its one-time point-of-sale retail database matches a name in one of its service divisions. The company then imports the information to both departments, so that when someone picks up the phone, they know that that person is active in both separate channels.
This is practical use of business intelligence - it helps you to actually do something based on all of the big data that you have scraped together.
Structure DataAnother major issue with big data is that companies often collect relatively unstructured data. Unstructured data may come in the form of paper or digital documents, raw or unrefined database resources, or even snippets of text and code from mobile devices. What unstructured data has in common is that it doesn't follow the relational database format. As a result, the traditional relatable database can’t handle it, and you don't get any business intelligence out of it.
There are two ways to handle this: grab a shovel and start digging, or get some resources that refine that unstructured data into actionable data. Companies that don't want to invest in new software may employ human hands to sort through unstructured data and format it correctly, but now you have some alternatives thanks to tools that will parse unstructured data effectively. Metadata, for example, is one way of automating data mining in a way that makes it useful.
Identify and Handle Data LakesAnother big buzzword in the big data community is data lake. Essentially, the data lake is just a large pool of data that's sitting there unused. It's the quintessential definition of data at rest - nothing is being done with it, it's not being disturbed, it's as icy and placid as the veneer of a stagnant body of water.
Again, there are many different ways to handle data lakes, but all of them start with reflecting on what's in those big data sets and why they’re in cold storage in the first place. Companies are building their own data centers and using ultramodern object-oriented data clustering technologies to break up these data lakes into actionable pieces. This is really done on a proprietary case-by-case basis, but some experts have suggestions about how to corral those data lakes into helpful canals that make pieces of information end up somewhere and do something.