There is nothing that hasn’t already been written about big data analytics and the benefits it renders on deliberate and exhaustive exploration. But its applications are ever increasing and it still presents marvel for humanity. For example, we are in the midst of a pandemic, one whose facets are still unknown even to experts like scientists, bio-technologists and medical researchers yet, technologies like big data platforms that curate data from medical registries and analysis of hidden patterns of the clinical trial have helped public health experts predict the spread of the virus and find treatments for its clinical management.
Against this backdrop, organizations are still unearthing ways to propel their business growth trajectory and big data analytics trends plays an infallible role in furthering this cause. Today, organizations are creating and dealing with massive volumes of data generated internally, from operational activities to sensors, CCTV footage as well as from external interactions with their communities – customers, users, partners, governing bodies, etc.
Big data has been popular among the businesses for years now and today, organizations do not demand evidence or proof to substantiate its value, unlike in the past when executives needed convincing and demonstrations to see how all their data could be of use. The narrative has now shifted – executives know that their closest competitors are exploiting it. For example, business leaders in the tourism and hospitality industries;
There are endless examples of horizontal and vertical applications of big data analytics and large enterprises and leaders are aware of its benefits. Then, where do the challenges lie?
For starters, enterprises struggle with collecting large amounts of data and that is because they are unsure of how to proceed with it. A clear goal-setting is still lacking as they are unsure of their analytics capabilities yet. And even when they start with this initiative, they look at it from a broader business perspective, we often hear ‘how do I gain more market share?’’
Additionally, organizations spend much time digging for gold mines within their data which can generate some value in-short term but could have a detrimental effect on the overall initiative. The right way to go about is to break down the problem areas and focus on rectifying the root causes. It would lead you to discover such gold mines (if they exist) along with guaranteeing some smaller treasures, snowballing into a much larger impact for your business. Not to overlook, it leads to operational efficiencies resulting in huge cost savings; after all, a penny saved is a penny earned.
What we have seen is that in most cases where enterprises are dealing with combining data from multiple sources, there is a lack of proper master data management and 20-25 percent of the impact could be delivered just by setting up processes, practices, and ensuring that people are aware of it. Big data comes in a variety of forms and from a variety of sources, what works for others might not be the best for your business too. You might be overlooking some data or missing ways to tackle some pieces of information you already have.
For example, according to a PitneyBowes report, a majority of organizations – about 80 percent – do not leverage the locational component of their data. This actionable insight could be valuable to a supplier ensuring that they save a lot by setting up distribution centers closer to their major customer base. Similarly, it could also be beneficial to a consumer goods company enabling them to adjust their offerings based on regional preferences.
So, it becomes important to identify the business use cases cutting across organizational silos. But how do you identify these cross-functional use cases?
According to a survey by Gartner Inc, more than 87 percent of organizations are classified as having low business intelligence (BI) and analytics maturity. So, what are successful companies doing differently that they are making headway with their data analytics? Quite simply, they are asking the right fundamental intent-driven questions that result in making data smart, meaningful, and innovative.
Starting with building a few macro-level processes can help companies turn vast amounts of data into insights-based use cases. And, instead of just focusing on short term targets, organizations should have intermediate and long term views/ goals in mind and create a strategy around that. For example, while recruiting candidates, one might ask, ‘how does one find good candidates?’. If a company looks deeper into its HR metrics, it will become clear whether to focus its recruitment efforts on external hiring platforms, or LinkedIn or its own career page on the company website. All of these activities can be optimized if enterprises can model their big data analytics to identify these underlying patterns and trends. Here are some ways in which an organization can evolve its capabilities for better business impact.
Develop a holistic data analytics strategy:
Right from mitigating risks to understanding customer behavior, identifying new revenue streams, and even reinventing their business models, an integrated analytics platform can help bring the organization together. Developing a holistic approach to data collection is imperative to replacing siloed tactical reporting. For example, given the focus on building smart cities, real-time data from a city can be harvested in an effective manner by merging analytics, smart systems (such as smart homes, etc.) and IoT devices (such as sensors, smartphones, etc.) to design sustainable cities of the future. It is important to use, collect, and analyze data generated from sensors, smartphones, mobile applications, etc. and implement solid evidence-based action plans.
High impact Big Data Analytics use cases:
Multiple-use cases help companies achieve a tech-enabled transformation at scale, across the organization. While this is a complex process, it is an absolutely necessary shift required to be made at the structural level. Some of the big data analytics use cases which organizations are heavily relying upon are – fraud prevention, setting up recommendation engines, customer segmentation, targeting, risk prevention, and more.
These practices not only help businesses generate new revenue streams but also get more bang for their buck spent during customer acquisition by extending the customer lifecycle, increasing the bucket sizes, resulting in your customers selling for you (via word-of-mouth).
Creating a culture of data management:
Digital transformation is a cultural shift – it is about creating a culture where data is at the heart of everything – human understanding, processes, and decision-making. Often decision-makers ask, ‘who would be the users of these big data systems and insights?’ We say, ’Everyone’.
The need is to enable and push everyone towards a more data-led approach starting from the executives at the top of the ladder to front-line managers along with associated vendors and partners. Why not enable and train frontline or field staff to do the necessary/routine tasks – like marking their attendance to feed their day’s number and quota, directly via a mobile app? The middle management can reflect on these numbers in real-time and proactively manage any disruptions. The top management, which is well-versed with business intricacies and having a hold on the data analytics systems would ensure no-value is left on the table. It would help enterprises attain exponential ROI growth.
While everybody knows that data is the new oil, there are very few people that know how to harness the potential of this resource. Understand that data is of no use if you cannot derive actionable insights from it. Collating the data and actually deriving actionable insights from the data are two different aspects for a business, to start with this might prove as a challenge (many of which we have discussed above) but with the right big data service provider, these aspects can be dealt with easily.
Here time is of utmost prominence until the businesses understand the importance of this and start implementing it their competitors might have already taken a big step forward by already implementing it. Thus by using big data for your startup enterprise you can get ahead in your quest. Big Data Analytics is a great opportunity and you can improve your chances of success by adopting the correct strategy.
Rishabh Rai is a Senior Consultant with the Marketing function at Polestar Solutions & Services India Pvt Ltd. He explores and covers the impact of data analytics and the changing technological landscape across industries. Apart from this, he holds a keen interest in various facets of marketing and can be found engaging in conversations on LinkedIn and Twitter on subjects such as branding, storytelling, consumer behaviour and more.