I believe Big Data has reached critical mass for most small businesses. When the volume, variety, and velocity of data that your business stores exceeds your capacity to make accurate and timely decisions, you have reached a tipping point. To successfully mine Big Data, your small business will need to do a lot of experimentation and exploration. Here are several factors to consider and approaches to mining this data.

Consider cost and locality

Big Data is too big to process using conventional means, and it’s too big to transport at a reasonable cost. So, how can you leverage this data cost effectively? Simple, move the program, not the data. For example, if you want to analyze the U.S. Census data, it is much easier to run your code on Amazon Web Services (AWS), where the data resides, rather than hosting such data locally. This is time efficient and free.

Locality of where data is frequently being updated can also be an issue. Manipulating such data from its source turns out to be the best approach in such situations. When it comes to cloud data manipulation, an inversion of priorities may be the solution to your Big Data needs.

Set a goal before mining

Without a concrete goal, the wide variety of data sources — Web, mobile, social, customer service, and sales interactions — could prove overwhelming. Your small business should first decide what its trying to accomplish with its Big Data implementation, whether its changing advertising strategy or improving customer service. These defined goals will make the task of mining Big Data more manageable, because you can start with only the data that will support whatever strategy you intend to implement that tie in with the specific goal.

Use customer intelligence to get good BI

You can understand each stage of your customer lifecycle by creating actionable customer interaction profiles; these will help with segmentation and drive your messaging. By involving customers in the blueprinting process, it enables you to get credible and instant feedback. After gathering the necessary data, you can present customers with early ideas or tests; this might result in more vital business intelligence that could lead to innovations.

Leverage the right toolset

Hadoop and MapReduce are go-to Big Data tools that are geared toward large enterprises. Tools that address data retrieval and interpretation in small business environments will offer a more tangible ROI for small-scale Big Data implementations. For instance, One such tool called SumAll measures if and how social traffic from different sources is converting to new users and new revenue.

Google Cloud Storage, together with the App Engine and Compute Engine, allows enterprises to start relatively small without having to build big data infrastructure from day one. By taking this incremental approach, your business can build up data sets in the cloud, which should help you ask questions that you wouldn’t have when dealing with on-premise solutions. In addition, Google allows users to store their data in a number of places: the Datastore for structured data, the Logstore for application logs, or Cloud Storage for generic data. Users can then transform or process the data using App Engine or Compute Engine. To gain insight into this data, users can query the data using BigQuery, which generates visualizations of data using various applications built on top of this data.

What tools do you use?

Tell us which Big Data tools your business uses and whether you’d recommend them.