Ready to learn Big Data Analytics? Browse courses like Big Data – What Every Manager Needs to Know developed by industry thought leaders and Experfy in Harvard Innovation Lab.
Big Data, Data Science and Analytics can simply be defined as the “art and science of deriving business value from data”. The central piece of this definition is “business value” which translates to either top line or bottom line impact that can be achieved through the use of data. The concept of data driven decision making has been around for years which made many businesses comprehend the importance of data. It was understood that if utilised correctly, data could remarkably elevate business’s ability to make insightful & strategic decisions.
A report by Harvard Business Review states that more data passes through the internet every second than was being stored on the web as a whole 20 years ago. Data generated in today’s world comes from disparate sources. It is high volume, high velocity, and high variety.Interestingly, almost 80% of the data that gets generated is in the form of unstructured text, image and voice data. To build sustainable competitive advantage and gain better customer understanding, every company must collect, store and leverage all available structured and unstructured data.
Big Data is affecting the business ecosystem in leaps and bounds. Following are some recent trends and developments which have made it an indispensable tool for businesses around the world.
Big Data driven Innovative business models:
“Big Data” has become the basic background of enterprise business model innovation. In the last decade, a number of extremely smart and talented entrepreneurs have created new business models using the power of web, mobile, sensor and other similar technologies – well known examples being Uber, Airbnb, Amazon, GE, eBay etc. Most of these business models are asset-lite models that use the power of information for creating differentiation. Big Data, Data Science, and Analytics has definitely been one of the key pillars for these business models. Having seen the disruptive power of these business models, traditional companies have started focusing on leveraging the power of each of the pillars of these business models.
Success of data driven strategies across multiple business functions:
A report by McKinsey & Company highlights how companies that have adopted data-driven strategies enjoy 5% higher productivity and 6% higher profits than their competitors. The insights acquired from analyzing bulk of data produced by the organisation can prove to be a game changer, particularly when applied to the strategic planning process. Multiple businesses performing varied functions can derive long term value from the use of data driven strategies. In around 1990s, this practice was pervasive in the financial services, telecom and retail sector. But the recent decade has seen many more sectors incorporate data analytics into their functioning and indeed benefiting from the approach.
The multi fold enhancement in technology capabilities:
Technology forms one of the most critical enablers for analytics to thrive, and is critical for the following:
- To capture data across multiple sources
- For plumbing the various data sources
- To store and retrieve data
- To process the data processing
- For building rules and patterns
- For implementing the rules and pattern
The tremendous development in IT systems in the last 15-20 years, have helped in making decisions at the most microscopic level. Essentially, technology has helped in enabling data driven decision making through Scope, Scale, and Speed.
However, before embarking on a big data journey, all businesses must also be aware of the plausible risks and investment in infrastructure and data collection. The single biggest risk that an organization may encounter is that of the lack of clarity of the business problem that is being addressed. Committing to the technology and human capital investment prior to identification of the business problems and the expected benefit is hence a major risk for any Big Data initiative.
The next biggest risk involves data quality and availability. Poor and inconsistent data may prove to be the biggest bane for the initiative. Implementation failure is another problem to look out for while undertaking a Big-Data initiative. A particular Big Data solution may not get implemented due to multiple hurdles including cultural barriers, non-acceptance from business owners and end users, technology challenges, regulatory challenges etc.
Businesses of all sizes must leverage the power of Big Data, otherwise, they are at serious risk of being left behind.
