By now we know that big data is important. It requires considerable investment, both in time and financial provision, to create a database that houses the collective insight on consumer behavior. It takes even greater commitment to sift through this data, confirm predictable consumer conclusion and draw unforeseen insight on our target consumer vs. our loyal consumer vs. the tidy description by which we qualified our consumer before looking at the facts.
One of the biggest questions companies face when embarking on big data solutions is how to organize its original organizational structure- the big data architecture. Depending on the intentions a company has behind running data analytics, its foreground can take several shapes and pathways.
Let’s look at a few of most common data modeling techniques and how to choose which one is right for your company:
To buy or to build
Do you want to build your own comprehensive database from scratch or utilize a big data system from an established data brand? Buying brings you the advantage of time. If you are extracting sales and consumer-centric analytics, buying a data software program likely suffices. The programs available for purchase are simple, easy to set up and work with similarly simple data (not too many types of data being analyzed). Working with an established company also provides the ease of adding new lines of data or extraction intentions as you grow together. Of course, this convenience comes a price.
Conversely, you can choose to build out your own bid data structure. Many building systems are cheap and user-friendly, but their complexities can take extreme patience and time. Building out a personalized data house is preferable to many companies who have unique approaches to capturing value from consumers.
To batch or to stream
Batching and streaming depends on whether you want descriptive data or predictive data. Batch produces results based on large volume data, so descriptions are thorough, allowing you to pull any insights you find.
Streaming data allows for predictive data. Streaming by nature opens a larger opportunity for scale and variety and makes it easy for you analysts and leaders to make quick, applicable decisions.
To go public or stay private
Utilizing a public cloud opens more doors and significant insight into many consumer behaviors. Before opting for public data housing, check in with your analysts. Are they comfortable making decisions on and from a public platform? If not, private clouds are an option. Private clouds are typically optimal for companies who need to be wary of security and compliance.
To be virtual or physical
While there is merit to adapting early and changing as technology evolves, many companies are still more comfortable with a physical big data infrastructure. Virtual infrastructure is growing, expanding and improving and in time, we will all likely transition to clouds. That said, if your company is in a vulnerable position and ill-prepared to go virtual, it is okay to stay physical. Focus on drawing insight from a data source that you can comfortably house and navigate. Virtual data might become inevitable and you must be prepared, but don’t throw away useful momentum just to adapt.
To go kappa or lambda
Kappa describes the architecture that runs all data as a stream in real-time and one that remains focused on data objectives. Changes in data are tracked in real-time, making results easy to understand and recommended for companies new to big data analysis.
Lambda data gets split into two pathways: one is quick and superficial, delivering insights immediately to analysts; the other path takes the data on a deeper dive through filters and analyses. While lambda is more thorough, it is more difficult to manage its two code bases.
Still not sure which data structure is right for you? Let us help you. Reach out to Quantum FBI’s team of data solution experts and find out which data infrastructure is best for your company and business intelligence big data objectives.