The dawn of the digital era with the commencement of unprecedented tech­nologies has resulted in a massive escalation in the amount of data that is generated and accumulated. Un­der the broad umbrella of “big data”, one must focus specially on useful data—data that is as close to real time as possible. Valuable real-time data must be segregated from huge pools of data that can help consumers and enterprises in their decision-making process. In enterprises, CIOs are often inundated with the data that is gener­ated by their own systems, data bases, and third-party start-ups which make it extremely complicated for them to ana­lyze and make crucial decisions that can significantly move the needle on their business. In order to identify trends and discern anomalies in data sets, the data must be ingested and normalized using potential software. Comprehend­ing the method to identify anomalies in data patterns is mission-critical. Over time anomalies tend to become a norm and hence it is vital to detect them at an early stage to help your company save substantial amount of money.

Is AI a Buzzword or a Requisite for Data Analysis?

Jumping on the Artificial Intelligence bandwagon, several start-ups are endeavoring to implement artificial intelligence in data pattern analysis by investing huge capital. Albeit, AI can help improve and expedite data-driven decisions, the important question here is “Is artificial intelligence the solution to your problems?” or is it just a fad that people are irrationally following? Instead of jumping to a rapid conclusion of leveraging AI to solve problems, enterprises as well start-ups must expend in advanced data base analysis which is not artificial intelligence. Also, it’s very important to ponder over the question that is inevitably going to rise in our minds—“Will machines run enterprises in future?” At its nascent stage, BlueRun is not equipped with a fully built out platform or the capital backup to invest an exorbitant amount in artificial intelligence. Maybe in the future when the company has enough capital turnover, implementing AI could be a viable solution to problems. Now or later, it is imperative to deliberate on the necessity of AI in building a solution and if it helps you to chase the expected amount that you are willing to sell your solution at. The next thing we thoroughly consider is whether our team is competent enough to develop winning systems in these areas. Our company is at its emerging stage and our team lacks the experience to build solutions by applying artificial intelligence that require years of expertise.

Conquering the Challenges of the Start-up Landscape

It is indispensable for a start-up to have a clear picture of the purchase cycle of a company and what is looks like to the CIO right now. There are other factors that are requisite to consider for a start-up to evolve into a full-fledged enterprise—deciding how mission critical the solution is to the company, how much money the company is leaving on the table by not adopting the solution. In terms of research and development, CIOs are myopic and do not allocate an innovation budget to explore and implement unprecedented technologies with start-ups. It is an inherent challenge that I am trying to help our companies overcome by building good relationships with CIOs, CMOs, and CTOs. It takes time as in a start-up world we move ahead rapidly whereas in the Fortune 500 world it’s a completely different bargain.

Emerging start-ups are always struggling to strike a balance between creating a solution that widely serves all kind of customers and picking a niche market that is concentrated enough to have a good wedge in an industry. Recognizing the potential of big data, many start-ups have begun to enter the big data field.