Big Data for Next-Gen Business Intelligence 

The use of Business Intelligence (BI) is a standard practice for most businesses of today, with enterprises relying on data analytics and visualization for generating business performance reports. BI software and tools transform raw data into actionable results that drive improved decision making and strategic planning, which in turn impact revenue generation and overall performance.

Traditional BI systems specifically use data from pre-defined, often heterogeneous, sources. However, with the advent of novel communication channels and digital platforms, there is a wealth of unexplored data such as customer buying patterns, feedback information and competitor intelligence, to name a few, that can be used in combination with the data generated from business internal processes to enhance the granularity of system intelligence.

This connotation creates a need for technologies that can find, access, explore and assimilate data from heterogeneous, multiple sources. Moreover, these technologies should also be able to handle the data volume, generation rate and complexity. This assertion is backed by Forbes’ estimate of growing demand for business intelligence services, stating that business data hence generated and needing analyses will escalate to 150 zettabytes by 2025[1].

Big data fills this technological gap by allowing real time capture and analysis of huge volumes of data available in different forms and formats. While BI focuses on answering enterprise-level questions, big data takes data-driven business decision making to another level by providing new information, backing answers to questions that businesses may not have even thought of asking. So, big data triggers out ‘of-the-box’ thinking and lets businesses reframe their questions such that they can discover unexplored terrains in their field and gain competitive advantage. 

How can big data & BI impact businesses?

Customer-centric strategies and personalization are at the core of growth and success for modern day businesses. When traditional BI data is combined with other sources of customer data, this reserve can be effectively used to develop predictive models using Artificial Intelligence (AI), making future planning practically foolproof. Marketing is the most discussed use case of big data for businesses, this technology can equally impact long-term decision making with forecasting models designed for specific scenarios and decisions.

Big data has had a huge impact on digital marketing and advertising, as it opens pathways for businesses to connect their websites with paid social platforms such as Facebook, to make cost-effective advertising plans. These systems empower high scope of customer acquisition with advanced AI-based segmentation analysis of users or prospective customers. 

Besides this, big data is one of the key enablers for personalization with businesses such as Amazon using it for improved customer experience and customized recommendations. The use of big data allows e-commerce firms such as Amazon to individualize user experience at the granular level, moving beyond simplistic variables such as demography.

Use Cases of Big Data-Driven Business Intelligence

Big data-driven BI can be used for a plethora of purposes. Broadly, these include marketing, sales, advertising and operations. Retail businesses generate a huge volume of periodic data in the form of transaction logs and sales receipts. Analysis of sales data can benefit a business in numerous ways by providing multi-faceted viewpoints. For instance, analysis of recent sales can provide insights on popularity of products among segments of customers. Moreover, this can also be squared with details of outlets that sold specific products and times of the day or year when these sales spiked or troughed.

Cumulatively, an analysis of this nature can help businesses strategize products to specific customers located in certain demographical locations at particular times. Trends analysis of this kind can help businesses diversify their SWOT (Strengths-Weaknesses-Opportunities-Threats) study with respect to specific products and services to back realignment of strategy and planning with data.

Similarly, big data can be used with traditional BI systems for inventory management to gain better comprehension of the operations and supply chain, which in turn shall drive improvements in operational efficiency to boost profits. One of the key improvements that big data offers to this use case is access to real time information for predicting possible issues and making timely interventions, in addition to informed decision-making.

The true power of big data lies in providing the big picture. Sales analysis and inventory management with big data can be cumulatively used to understand customer demand patterns and align them with inventory to accommodate, for example, seasonal patterns. In this manner, big data-driven BI can help streamline demand-supply for businesses, with optimized costs and enhanced profits. In addition, together big data and BI can back customer acquisition and retention by segmenting groups, to identify customers with high potential and tweaking loyalty programs and offers on the basis of customer churn analysis.

Business intelligence with big data is capable of revolutionizing disparate business sectors including retail and e-commerce. Although, common case studies of technological efficacy of big data for businesses feature big players such as Amazon and Walmart, this technology can add great value to medium and small businesses with substantial enhancements in operational productivity and revenue generation. SOLVD can help you explore this potential for your business. Please email us at solvd@wlv.ac.uk if you wish to be notified of future digital technologies events for SMEs, or to get in touch with our experts. 

[1] https://www.forbes.com/sites/rkulkarni/2019/02/07/big-data-goes-big/?sh=3ae015fd20d7