The global pandemic has brought about a historic transformation across lives and livelihood. We are living through an explosion of remote working models, OTT platforms, online shopping and socializing through collaborative platforms, and so much more, to meet the new reality of a post COVID-19 world. Today, with more than 4.66 billion internet users worldwide[1], organizations have access to massive volumes of data that can help them in making the right decisions to move forward in the new normal.
Data is proving to be pervasive, necessary, and invaluable to manage and measure all aspects of a business. A company that successfully uses big data and analytics is Netflix[2], one of the biggest online platforms for streaming movies and TV shows, which has grown to a whopping 203 million subscribers during this period. The secret behind their success is the deep insights they have into the ever-evolving user preferences, and their ability to consistently provide what their customers are looking for.
Data Analytics – An Inflection Point
Today, the need of the hour is to redefine the speed and accuracy of how data is tracked by the companies and then analyze and utilize it meaningfully to stay ahead of the curve, re-strategize business modelling approaches for operational agility and business continuity, and develop novel data pipelines to account for new data. Additionally, benefits such as its ability to forecast demand, identify potential supply-chain disruptions, target support services to at-risk workers, and determine the effectiveness of crisis intervention strategies, are some of the many other reasons driving its adoption.
Further, with an accelerated adoption of digital transformation, technologies like Artificial Intelligence (AI) and Machine Learning (ML) are fast becoming major drivers for automation of services and business processes, aiding pattern predictions and offering actionable insights, leading to higher sales and better customer experience. An example that is very relatable[3], YouTube’s recommendation algorithm drives a whopping 70% of the content watched on its platform.
Transformations within Analytics
Business intelligence has undergone a massive revolution in the last few years. Take for instance, embedded analytics, where data analysis occurs within a user’s natural workflow, without the need to switch to another application[4], offering quicker and more effective actionable insights and decision-making. According to Allied Market this segment is expected to reach $60.28 billion by 2023[5].
Additionally, there is greater emphasis by organizations on expansion of existing pool of data sources to create new data pipelines and use existing data in new ways for better insights and outcomes. In order to be future-ready, organizations need to plan for a range of probable scenarios rather than one or two predictions and here advanced analytics with the introduction of multiple scenario analyses play a pivotal role.
What to Consider before Adopting an Analytical Model
Choosing appropriate analytical tools. An analytics tool is only as effective as its fit for an organization’s needs, and should be capable of supporting planning, business intelligence and predictive analytics in a remote working environment. In making any analytics investment, decision makers need to analyze its features, support of industry best practices, customization capabilities, ease of use, cost, and pricing, in addition to some of the factors discussed below.
Data security and privacy. It is essential to address issues of data security and privacy in an analytics solution before its adoption, especially given the dispersed and remote working environment. It is important to understand if there is multi factor authentication, whether or not there are adequate security measures in place, and policies that manage the fallout in case of a breach.
Ability to support remote teams, especially today where it is normal to have some teams working from home while others work staggered schedules in officesacross functions like supply chains, marketing and sales.
Ability to collate data from disparate sources. With remote working being the new normal, it is important to have one source of truth for any analytics solution, so it is important to understand if the platform under consideration has the ability to connect to all the data sources and generate real time analytics and insights for decision making and analysis.
User Friendly reporting and dashboarding capabilities. In today’s cloud and remote working environment, an analytics solution that enables its business users to slice and dice the data they need without IT or technical involvement makes its adoption easier and more pervasive in an organization.
Data ownership. Ownership of data should be an important consideration for an analytics tool, especially in the cloud. Some of the factors to consider include whether or not the solution enables you to maintain ownership of the data in a secure manner and if it facilitates the transfer of data back to you at the end of a contract.
Data analysis can help organizations better understand their customers, evaluate their marketing strategies, develop personalized products and services, and capture bigger piece of the pie in the market by attracting new customers.
[1] https://www.statista.com/statistics/617136/digital-population-worldwide/
[2] https://economictimes.indiatimes.com/industry/media/entertainment/media/netflix-keeps-growing-in-pandemic-tops-200-mn-subscribers/articleshow/80357680.cms?from=mdr#:~:text=the%20coronavirus%20pandemic.-,The%20streaming%20television%20leader%20added%20some%208.5%20million%20paid%20subscribers,market%20trades%20following%20the%20release.
[3] https://knowledge.wharton.upenn.edu/article/marketing-future-data-analytics-changing/
[4] https://www.gartner.com/en/information-technology/glossary/embedded-analytics
[5] https://www.datapine.com/blog/business-intelligence-trends/
