If you plan on sending a spacecraft far out towards Pluto you have two options.
You could load up on massive amount of fuel, and burn it all in an attempt to overcome the gravitational pull of the different bodies. It would be mind bogglingly expensive and still won’t work.
Or you could use gravity.
Contrary to what the person on the street assumes, gravity can actually be used to propel a spacecraft forward.
Using a maneuver known as gravity assist, a spacecraft can approach a planet, fall into its orbit and instead of crashing into the surface change direction and gain even more velocity. This was how Voyager 1, launched by NASA in 1977 flew out of the solar system last year using the gravitational fields of Jupiter and Saturn to propel it forward.
Come to think of it, Big Data is very much like gravity.
If you know how to utilize Big Data, you could be more nimble, do more with less, become smarter and actually predict what the customer wants even before they ask for it.
Neglect it, or do a half baked job at mining insights from Big Data, and there is a good chance that you may go belly up.
The discourse around Big Data often focuses on Fortune 500 companies. While these companies, by virtue of their size and scale do generate gigs of data daily and are potent proving grounds for Big Data analytics, they are not the only beneficiaries.
Even in small businesses, the effect of Big Data can be transformative.
For example, a real estate agency maintaining holiday homes could use Big Data analytics to offer homeowners weekly pricing recommendations based on tourist flow, weather, and market conditions, ensuring that occupancy levels are high.
A zoo can predict the number of visitors on a particular weekend and consequently the number of temps it needs to hire based on historical data about local climate conditions. It can analyze the times ticket sales spiked to offer discounts and flash sales, bumping up memberships and bringing in more money.
Data science is a young field, and data scientists are in high demand: McKinsey has stated that by 2018 United States itself will face a dearth of 140,000 to 190,000 analysts and 1.5 million Big Data savvy managers.
If you are a big company you will have enough resources to hire an in-house team of data scientists. Smaller businesses whose bread and butter is data crunching will also make do.
MIT Sloan Management Review calls such companies analytically sophisticated companies.
On the other hand, the vast majority of companies including small businesses are analytically challenged companies. These companies either don’t have a data based culture of decision making or they have no idea where to start if they want to use their structured, unstructured, and semi-structured data to make business decisions.
While there are a number of tools available in the market for analyzing data, it may still not be enough for them.
These companies have one solution: outsource their analytics. Outsourcing analytics, if done properly is low risk because you don’t have to invest resources in building an in-house team, or invest significant resources and then finding out that you are doing it wrong.
Before you start the process of outsourcing data analytics, you need to prepare for it.
Here are a few questions to keep in mind.
1) Does your business even benefit from Big Data analytics?
While all businesses benefit from data based decisions, Big Data is more specific and some businesses, by their very nature won’t really need to worry about it. For example, if your business is just starting out, or it does not generate terabytes of data yearly, you can still work with Microsoft Excel and make data based decisions.
2) Do you have a culture of evidence based decision making?
Big Data is merely a tool. Unless there is a culture of listening to data, instead of to the gut while making decisions, no amount of analytics will have any impact on your bottom line. Before considering a move to Big Data analytics outsourcing, review whether you are utilizing the data and insights generated by conventional tools like ERP and CRM suites.
3) Is your raw data clean?
Garbage in, garbage out: the insights from analytics will be as good as the quality of raw data. If you don’t have data governance systems in place, and if you don’t clean up the data before handing it off to be analyzed, you will get results that make no sense.
4) Are your business rules consistent?
Do you have systems and processes in place that take the human factor out of routine decision making? Is there a consistent policy on discounts and rebates that are offered to customers, or is it left to the whims and fancies of individual employees? Is there a return policy, and is it strictly followed? Without such rules, insights from analytics would be wasted.
Big Data can help small businesses punch above their weight and corner a disproportionate share of the market. But for it to truly make an impact in revenues and profits they will have to get their house in order.
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