Data Strategy Book Reflection

Data Strategy Book Reflection

Author: Bernard Marr

Writer: SS

Bernard Marr has made the concept of “Big Data” simple for the people to determine how data can be utilized to add value to the business. People have a misconception if they do not hold any sort of business then collecting data and analyzing it, do not apply to them. But, Bernard Marr has corrected this misapprehension through his book. Even if a person is not the business professional, one can analyze the data around himself. For example, a person can track and analyze the data stored by the health monitoring wearables (Apple watch), applications (My fitness pal), and home-based devices (weighing scale) to improve the health like blood pressure, diabetes, weight etc. According to the analysis, they can change their diet chart, exercises, and daily habits. The book is a brief guide reflecting the importance of data on decision making, business operations, profit, and future growth. According to the International Institute for Analytics, by 2020, the business utilizing data will see $430 billion growth in productivity over the competitors. Bernard Marr has explained how to build a good data strategy to turn data into useful insights for the exponential growth in the future.

A good data strategy should start by identifying the business priorities and what questions business wants to answer from the analysis. For example, sales data analysis is effective for measuring the profit but is not quite appropriate for employee churn analysis. To identify the business objectives, Bernard Marr has listed four critical areas to look before forming the key questions - 1) Customer, market, and competition; 2) finance; 3) business operations; and 4) people (employees). If a business wants to increase their customers for a specific product, first they need to identify the current customers, similarities between them, geography, purchase patterns, demands, and interests. For example, MAC Cosmetics wants to launch a new series of foundation palette, then first the company needs to monitor its customers’ behaviors (let’s assume women); secondly geographic location (which type of the shade does an area requires the most); thirdly customers frequencies of buying the foundation (one of the factors can be product price), and so on. Then the company can design questionnaires based on analysis and offers discounts/coupons to the valuable customers. So, to make better business decisions, define the goal and then explore the data related to the objective.

Next, Bernard Marr explained from where to acquire datasets for the analysis purpose. The company either can use its internal database or can buy data from data brokers and other huge companies like Apple, Google, Microsoft etc. The big companies like IBM have massive manpower and budgets to buy other companies datacenters (e.g. Weather.com). Then they monetize the data by selling it to different industries like agriculture, transportation, construction etc. who need weather-related information to carry out their business. This provides a good opportunity to earn money on data as an asset, by making it available to the customers and smaller companies. But, external data comes with the ethical challenges like ownership, privacy, honesty, and transparency. Bernard Marr has beautifully explained the concept of data governance to minimize the ethical issues. Data governance involves data minimization, metadata updating, encryption, and letting customers know what data company is collecting, for what reason, and with whom it is going to be shared. This type of approach builds trust in public and allow customers to happily share their data with the companies.    

Analysis: Bernard Marr has done a commendable job in turning such a complex content into an understandable format. The book has easy and clear vocabulary helping even the novice to understand the benefits of data and its analysis to the business. Bernard has effectively communicated his knowledge and experience through the real-life examples, situations, and practices adopted by the companies like IBM, Google, Fitbit etc. After reading the book and understanding how much data companies are stockpiling, it is shocking to realize that less than a percent of that gathered data is analyzed and used. But, I liked the fact how various companies are working with data to create something new, for example, Facebook automatic tagging. I remember when I had to manually tag my friends on my uploaded photos, but now I realized the story behind this new development. Facebook integration of facial recognition capabilities to the social network by acquiring Face.com is a unique example of utilizing right kind of data and boosting the productivity. I agree with the author, how company should be transparent with the customers, taking permissions before gathering the data, and how users are ignorant about the privacy etc. but in some cases, companies leave no choice for the users. There are some applications which require users to sign-in from their Facebook/Google accounts to access their personal data like images, contacts or location. In my opinion, such applications should refrain from forcing a customer to reveal their personal data just to utilize some services.