Big Data on an Itty Bitty Budget

Big Data on an Itty Bitty Budget

By Lloyd Marino

More now than ever, organizations, both great and small, need big data. As I lay out in my white paper, growing numbers of people are recognizing that more information—when properly captured, analyzed, and acted upon—means more targeted customer outreach, more expanding successful strategies, more production savings, and most importantly, more profits.

But when most folks think about big data they probably equate it with a big price tag. Indeed, when I talk to clients about leveraging big data and big data analytics to grow their business, I often see little beads of sweat gathering en masse above their eyebrows, and a slightly panicked look crossing their faces. “I’m not Exxon or Walmart. How can I afford big data,” is the standard response.

I swallow my immediate reply of “How can you afford NOT to use it,” and tell them how to tap big data’s power on an itty bitty budget using CAAP, my simple 4-step strategy for maximizing big data.

  1.    Capture your data

Companies have more access to data than most actually collect. Yes, you track sales, but what about inquiries? You probably have a list of emails of people who bought at your website and an email list for newsletters, but do you track all visitors? Talk to your web designers about tracking what pages are most viewed, how long visitors stay on the page, and what items get the most clicks, even if they don’t result in immediate sales. This can show you what to focus on in the future. You can discard items that receive neither sales nor clicks, while investigating why clicks on other items do not result in sales.

You can also collect more data on your customers. Ever take a quiz online? Such as, “What Pokémon are you?” or “Can you beat a fourth-grader?”  All those quizzes secretly collect data. You can develop an interactive quiz tied to your business that feeds you information about your visitors. This can be fun for your visitors too. You can also conduct a more traditional survey if you want to be more serious.

It is also possible to get data from other sources. Google analytics can provide information about website visitors and where they come from. Facebook also has data available. The government has many sources of data to be mined, including census data.

  1.    Analyze your data

Data alone does little until it is analyzed and interpreted. This may be where the biggest expenses will be found. Sophisticated databases like Oracle cost money as do people trained in how to make them work. However open source software like Hadoop and the R programming language can help do data analysis on the cheap.

However, even with open source software, someone has to program the database and run the analytic tools. Most small businesses would find it easier to outsource this to a company like 1010data or a service like IBM Watson Analytics, rather than hire a full-time specialist.

What types of analytics you need depend on what questions you have and what data you have collected. Generally, the analytics will be different if you are trying to increase sales, cut costs, or speed production.

  1.    Act on analysis

The third stage is to use the analysis to act. Frequently, the people making the decisions are different from those who conducted, or even commissioned, the analysis. That means the analyst has to develop a way to communicate the results clearly to non-specialists. This requires not only understanding the data and analysis, but also how this relates to the needs of the company and the problem needing to be solved.

Visual elements can help. Some analytic programs provide dashboards that make it easy to see where the data is pointed. Programs can also produce charts and graphs that can show how much money is currently being lost and how much more can be made with changes.

  1.    Profit

Of course, we’re not just playing with data for the fun of it (well, not completely). The data should be used to drive decisions that lead to more sales or savings. Some managers may need to surrender their assumptions when they fail to match the research findings. It may be helpful to develop a plan ahead of time on how much data (two months, three, four?) is needed before it sways decision-making.

Future of Big Data on a Budget

One thing I’ve learned after decades working in the field is that technology is becoming faster, more furious and cheaper. These are good things, of course. While databases once dealt with gigabytes, and now use terabytes and petabytes, even larger datasets are on the horizon. While your company may never need data in that size, other companies will and that reality will lower costs for everyone.

Inevitably, the software tools will evolve, becoming both more powerful and easier to use. We see this in other areas like video-editing as well as basic spreadsheet software. In the past, Microsoft has updated their products to incorporate features from competing standalone products. So I would not be surprised to see them add more “big data” features to Excel. Software from other sources will also gain in sophistication as they move from an esoteric procedure for stats experts to a tool for ordinary businesspeople.

Of course, there will always be programs and services available for those with stratospheric amounts of money that cannot be duplicated by those with more earthbound budgets. Even so, businesses with small amounts of money can do more with big data than even the largest company would have imagined a decade ago.

Image By: Helloquence

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