Data Science and Analytics

Data science and analytics in the digital economy

Technology is driving the new economy. Today, you can witness a profusion of businesses that would not have been conceivable before the first decade of the twenty-first century, because the infrastructure required to make them function was still evolving. With more and more storage and processing power now being available in smaller devices, artificial intelligence, machine learning, and neural networks, among other advanced technologies, are uncovering newer opportunities in businesses and revealing previously unexplored potential, and this is happening at all scales. Large scale adoption of digital technologies is generating massive amounts of data as a by-product, which must be analysed and comprehended to generate insights. According to studies, by 2025, the digital economy would account for 24.3 percent of global GDP, with a data output of 463 Exabytes.

What exactly is the Digital Economy?

The digital economy encompasses every transaction done online across businesses, people, data, devices, processes, etc. The backbone of this economy is hyper-connectivity the pervasive and growing interconnectedness that is a product of the internet, data mobility and Internet of Things (IoT). Apart from making new business ideas possible, it is also transforming conventional business practices – organizational structures, transactions, interactions, consumer behaviour and use.

The role of data science and analytics in the Digital Economy

The massive surge of data has thrown a spotlight on the need for Data Science and Analysis. Data Science tackles enormous sets of data and comprises the disciplines of data cleansing, preparation, organization, modelling and ultimately analysis. It is a combination of multiple subjects – Mathematics, Statistics, Computer Science, Information Science, Machine Learning, Artificial Intelligence, and Decision Making. Data analysis helps in making better business decisions by using existing information and statistical modelling techniques to uncover actionable insights. The path involves checking hypotheses, data preparation, model training, model testing and extracting specific insights.

How is data science and analytics leading the new economy?

With zillions of data points being generated every day, data has surpassed the value of oil. Data Science and Analytics (DSA) is an enabler for scale and growth in business, and supports it with accurate, data-driven decision-making. 

When data science services such as machine learning or AI are used, an organisation’s data becomes a gold mine to generate insights that help make decisions to improve business outcomes.When an organisation invests in structuring its data, it improves the efficacy of predictive analysis and helps in more precise predictions of what is to come.

‘Data-driven marketing’ is an all-encompassing term these days. It is only with data that one can accurately identify the challenges in sales and marketing, judiciously define budgets, target the right customer segments and bridge the gap between sales targets and actual sales. The combination of data from multiple sources provides more precise insights for an organisation, helps them map the consumer’s journey, the touchpoints of customers with a specific persona and so on.

Marketing teams can use real-time dynamic segmentation to profile customers and make tailored recommendations. Targeted consumer outreach maximises budget and boosts conversions. Customer journey and lifecycle optimization help map, analyse, and recommend next-best actions. To build a churn propensity model, the team must first identify “at-risk customers” and “churn sources.” This solution is highly customizable. For instance, telecom customer service reps may know a customer’s purchase history, subscription type, and contact preferences.

Product recommendation and affinity models can predict a product’s sales. Sales teams can assess product bundling and cross-sell/up-sell opportunities. Lead conversion optimization boosts sales funnel conversion by analysing successes and failures. Shelf performance measurement and optimization measures and optimises digital product performance. Customer evaluations and comments are used to assess the success of the company’s products and services, along with out-of-stock conditions, price sensitivity and price elasticity, impact of product marketing, content effectiveness, gap between self and competitor products assortment, and sales estimation. This solution aims to increase market and search share. While meeting corporate and industry requirements, sales teams can use price optimization to make key decisions like product pricing. 

Operations teams can utilize a Risk modelling solution to assess and categorise various business hazards. Within the risk profile summary, it highlights the primary risk factors and suggests possible remedies. Demand, labour, inventory, and other business elements can be forecasted. This approach seeks to deliver recommendations based on precise gap and driver evaluations. The Command Centre is a drill-down anomaly detection and early warning solution based on a data lake architecture with aggregated levels and a consumption layer tailored to the COO office’s needs. 

Data scientists can help locate data sources and construct automated dashboards that gather all pertinent data in real-time. Organisations can now understand their customers’ issues and evolving demands, and tailor products accordingly. Manufacturers can use DSA’s digital sensors to track and correct production waste, quality control concerns, and other challenges. With this information, businesses can adjust faster and make better judgments.

Companies used to categorise clients into broad segments. DSA now uses data science, AI, and third-party data to investigate things including client affluence, market demand, and behavioural tendencies. It also helps businesses understand their business and market better, and it helps them see their future. The sooner a company adopts a DSA oriented strategy, the better, as time is the only competitive advantage. Because DSA is so widely used nowadays, your competitors can easily reap the benefits.

DSA is not free of challenges, however. Overload is an obvious one, with multiple data sources contributing to a low-quality data pools. Threats to data security is another area which should keep the leadership team alert. Since data is the new currency of doing business, this needs to be protected and sometimes the cost can be high, if data security is not planned well. A data scientist designs models that help predict accurate results, but only if the underlying data used to do so are appropriate. Opting for a mechanical approach to DSA that identifies datasets and performs generic analysis can be less effective.

The future of Data Science and Analytics

From innovation to the decision-making process, data lies at the heart of it all. As more and more organisations adopt data science and generate massive amounts of data, there have been calls for legal and ethical compliance within every sector of the economy. In the future, DSA will see some more fine-tuning and compliance requirements, but these are only expected to encourage synergy and growth.  

The article has been written by Hoshi Mistry, Principal and Head of Operations for ‘Digital Services’ and ‘Customer Operations’ at eClerx Services Limited

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