Connected Retail – Modern Business Intelligence and Analytics

By  Mohit Sharma, Manager-Digital Transformation Services, Sasken Technologies Limited

This paper by, Sasken Technologies Limited, discusses the need for relevant and economic solutions, emerging trends, and accompanying challenges that arise with growing complexity and evaluates the best-possible mechanism to overcome these challenges.

Keywords—Retail, Connected Retail, Omni-channel, Modern BI, Analytics

Mohit SharmaIntroduction

It’s the Era of connected retail. Connected retail is the Omni-channel along with Modern BI and Advanced Analytics. Apps, Location positioning, Interactive and socially connected displays, Touch Screens, Smart Surfaces, In-store wifi, Augmented reality, Mobile POS/checkout, Click and collect are among the connected retail landscape. All of them are generating huge data, which need to be processed, analyzed in real-time to remain competitive in the business. This paper discusses the need and components for Modern BI and Advanced Analytics in the connected retail world.

Retail Challenges

Business encouraging technologies- RFID Tags, NFC, Virtual Reality, Augmented Reality, IoT (Internet of things) and next blockchain. A lot of Sensor data and other data is accumulating every second and retailers need to drive the business value in real-time from it.These are the major challenges that retail is facing in one or in another way.

  • Business integration
  • Technology integration
  • Competitive differentiation
  • Managing real time interaction
  • Fragmented data and data analysis
  • Personalized Interaction with customer
  • Targeting prospects, loyalty and retention
  • Recognizing customers across touch points

 Role of Modern BI and Analytics in Retail

Global Market of business intelligence (BI) & Analytics software is forecast to grow to $18.3 billion in 2017 and $22.8 billion in 2020”. – Gartner, Inc.

To remain competitive in the race, Retailer needs to implement Modern BI and Analytics. A glance at the most valuable Brands by Forbes in 2017, five of the top six most valuable companies have either built their entire business model on data or are heavily investing in data.

The World’s Most Valuable Brands

  • Apple (manufactures some of the most iconic products in the world)
  • Google (a media company)
  • Microsoft (a software giant)
  • Facebook (social network platform)
  • Amazon (is a retailer ) also joined the top 10 in 2017, jumping to sixth place


clip_image001Component of Modern BI and Analytics

Modern BI and Analytics is shifting the market towards Business-led, Agile analytics & self-service from IT-led & system-of-record reporting. Components of the Moden BI and Analytics are:

Self – Service BI

Self- Service Business Intelligence allows users to generate custom reports and analytical queries without the help of the IT department, which will allow them to focus on other tasks at hand. This will allow the users to access and work with data analytics without the help of the information technology department.

Embedded Analytics

Embedded analytics is the use of reporting and analytic capabilities in transactional business applications. These capabilities can reside outside of the application, reusing the analytic infrastructure built by many enterprises, but must be easily accessible from inside the application, without forcing users to switch between systems. The integration of a business intelligence (BI) platform with the application architecture will enable users to choose where in the business process the analytics should be embedded.

Guided Analytics

Unlike other BI tools that enforce preconceived hierarchies and preconceived notions of how data should be related, the Guided Analytics app allows users to explore the data from any angle, while also guiding the them to the answers they seek, providing a level of flexibility and control you won’t find with other data visualizations tools. This will allow them to answer additional questions that arise and to see the whole story that lives within their data.

Customized Applications

Whether you operate your business exclusively from in-house systems, or whether you are currently in a state of “transition to the cloud,” all modern applications require some degree of design, development, and customization. No longer running in silos, critical applications have to be interoperable, capable of exchanging information with other solutions, and flexible to meet changing business needs. As a result, true “Buy vs. Build” decisions are a thing of the past.

Integrated Platform analytics

An integrated analytics platform is an integrated solution that brings together performance management, analytics, and business intelligence tools in a single package. It provides an end-to-end solution for delivering business intelligence from multiple fronts and gives the user a clear visual representation of data as well as providing services such as revenue calculation, forecasting and developing marketing strategy models and algorithms all on the same system, allowing for interoperability.

Data Governance

As per GDPR 100% compliance required on May 25, 2018, and fines of up to 2-4% of global revenue for non-compliance, the pressure is on to comply. While data governance initiatives can be driven by a desire to improve data quality, they are more often driven by C-Level leaders responding to external regulations. Examples of these regulations include Sarbanes-Oxley, Basel I, Basel II, HIPAA, GDPR, cGMP[4], and a number of data privacy regulations.

Market drivers
  1. Cloud Technology adoption

Increasing adoption of cloud technology among the business can be viewed from:

  • In 15 months, 80% of all IT budgets will be committed to cloud apps and solutions.
  • Hybrid cloud adoption grew 3X in the last year, increasing from 19% to 57% of organizations surveyed.
  • Public cloud platform adoption is highest in services companies (28%), with engineering (30%) and government (29%) having the highest adoption of private clouds. [7]

Growth of advanced analytics

Nearly two-thirds of companies with well-established advanced analytics strategies report operating margins and revenues of 15% or more, according to a report developed by Forbes Insights, in collaboration with EY.

Adoption of data-driven decision making

Business demanding HiPPO (Highest Paid Person Opinion) must be backed by the data as a study from the MIT Center for Digital Business found that organizations driven most by data-based decision making had 4% higher productivity rates and 6% higher profits.

Emergence of IoT-enabled technologies

IoT data analytics, Big data, and Machine Learning. A second evolution in the context of Big Data, analytics, and machine learning is the rise of new tools for streaming data analytics. Both evolutions fit in a context of enablement of leveraging IoT data to feed all sorts of artificial intelligence applications, including the feeding of machine-learning engines.

Market Restraints and Implementation Challenges
  • Past of IT-Led systems
  • Existing Investment in Silos systems
  • Initial High Investment
  • Lack of Skilled workforce
  • Management and Maintenance of data quality
Opportunities and Moving Ahead
  1. Positive ROI

Business Leader in a retail industry demands Modern BI to back up marketing spending decisions. No wonder CEOs are turning to marketing models and advanced analytics to improve the ROI on their ever-growing list of marketing programs.

Embedded BI

Embedded analytics (or embedded BI) means adding features normally associated with BI software – such as dashboard reporting, data visualization, and analytics tools – to existing applications. This can generally be achieved in two ways:

  • In-house development — i.e., the app manufacturer builds its own analytics platform and includes it in its existing product
  • Purchasing and embedding out-of-the-box software — i.e., turning to an external developer and integrating its analytics solution in the application
Insights from data lake

Data Lake is an object-based repository that stores a large amount of raw data right from the structure, semi-structured and unstructured data in its native format. As opposed to a data warehouse that stores data in files or folders, Data Lake is marked by its horizontal flat architecture where each data element is assigned a unique identifier and is tagged with extended meta tags.

The Retail business is generating a lot of data so the next big step is, obviously, to gather insights on this collected data.

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