When we talk about Big Data, we mostly refer to large enterprises, capable of affording big data products that are quite expensive or resource-intensive. Small businesses and startups are often out the picture. Yet, budding business ventures can employ an effective big data strategy and embarking on a data driven strategy with huge success. Here is how!
Industry leaders like Amazon and Pandora have made some serious headway in mining “big data” for their benefit. They have the resources and intent to dig deep into huge datasets to come up with innovative solutions and business strategies for increasing their customer base and revenue stream. There are no two thoughts about the fact that huge potential is there to be tapped. Startups are beginning to recognize this potential and are therefore now taking extra efforts to take a dive into the big data ocean. However, without proper guidance, it’s too easy to lose your way here. Here are some tips for startups to put their best foot forward:
1. Bring in experts
In technology, evolution is happening a way bit too fast. This can create a knowledge gap where your understanding of problems and solutions is primitive as compared to those faced by the industry or your own startup. Thus; the requirement to bring in experts. Most recently, there is an emergence of new roles such as data scientists and chief data officers. Many would term this unnecessary overhead expense, but for startups who are venturing in big data, these are valuable assets. As Gartner puts it; 25 percent of all large global organizations will have one CDO by 2015. Therefore, hiring a Big data professional who is academically sound and has gone through a series of analytical courses should be on top of the list of any entrepreneur.
2. Start small and scale fast
One of the biggest mistakes that especially startups tend to commit is simply biting off more than they can chew. The current emphasis in the corporate world is to move quickly and decisively with all available data to increase a company’s competitive advantage. There are too many startups that take on more than what they can handle successfully simultaneously. Starting small and scaling fast is the right way of going about all big data projects. Once teams are comfortable with the solutions, you can grow the project quickly, reach completion in timely manner and yield the desired results.
3. Brace for initial hiccups; Prepare well and be patient
According to Sean Anderson, manager of data services at cloud computing company Rackspace, the big data landscape is changing at a feverish pace and most experts would agree with that. According to a recent Gartner survey, 73% of enterprise respondents plan to invest big time in the next couple of years in big data. However, only a minority of these organizations could experience a significant boost in their revenues. The key takeaway from this statistic is that big data is not a piece of cake even for the smartest organization, leave alone the startups.
4. Give each of the three V’s of big data (Volume, Velocity, Variety) equal attention
Mostly experts have defined Big Data through instruments of the three V’s , volume, velocity and variety. Volume is the sheer scale of the data. Out of the 7 billion people on earth, 6 billion have mobile phones creating gazillions of data each day. According to IBM Big Data & Analytics Hub, 40 Zetabytes of data will be created by 2020, 300 times of what it used to be in 2005. Velocity is obviously the speed of the streaming data.In the age of wearable tech, smartphones and “smart-everything” there is a constant stream of real time data to be analyzed. Variety refers to the different types of data that your database allows you to query, including unstructured data such as audio, video and email text.
5. Don’t follow the crowd
Everyone going berserk about big data doesn’t mean you have to also include it in your business strategy. May be you don’t even need a big-data strategy at all. As they say, if it ain’t broke, don’t fix it. There are a number of companies out there that spend a lot of time analyzing big-data technologies only to find later that their existing technology suffices more than enough.