Digital Transformations: How to Compete and Win against the Big Data Guys

Mitesh Athwani
Published 11/09/2021
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Digital TransformationIt’s natural to assume that the “FAANG” companies, Facebook, Amazon, Apple, Netflix, and Google (Alphabet), gather and use Big Data to make big decisions. They’re big companies. While they are not the only companies that collect Big Data, they and other organizations also buy and sell it. Sometimes they even share it with other vendors in what is increasingly known as the “data economy.”

As the data economy expands, data-driven players need to carefully consider the rules of engagement. First, are they using their data in alignment with the company’s mission and vision? Second, have they addressed the issue of data governance? Is the company complying with best practices and established regulations? If not, big companies could find themselves being edged out by other (perhaps) smaller companies that have more clarity around who their customers are, what the end-users and prospects want and need, and how their company will answer those needs.



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Data collection has limitations depending on resources and technology, which can make refining data somewhat of a constraint. On the other hand, squeezing the juice out of existing data (which some companies don’t realize they have) and then repurposing the data with intention can yield surprising insights. For this reason, smaller companies have a chance against the big guys, as was the case when Google acquired the user-submitted data map company WAZE. WAZE turned out to be a unicorn company with hungry investors anxious to buy a piece of their growth.

The bad news is that it’s hard to see what lens to apply to mountains of raw data. The fact remains, business is experiencing a brave new world and is at a tipping point where the focus needs to be on using data responsibly to create a better future with positive outcomes.


Three steps to a successful digital transformation

As companies work through their digital transformations, they need to understand what is required and how they can miss the mark if they insist on using data to prove their point. It’s the other way around. The best way is with a clear roadmap of initiatives measured through KPIs that align with the target objectives. Consider these essential steps:

1. Decide to do it. Going digital isn’t easy because it requires an altered way of doing business and thinking differently. Beyond a transformation mindset, it’s best to prepare for large capital outlays for hardware and software. Plus, organizations must hire people with the education and experience to organize, manage, and analyze the data so their leaders can make the best decisions for serving customers and the marketplace including stakeholders (such as investors), and citizens (global impact).

2. Avoid data silos. Once the decision is made, and the technology and resources are in place, organizations often err by using traditional data architecture that captures wide-reaching data that are ingested, stored, and analyzed in silos—limiting the data enrichment possibilities and potential learnings from analytics. Here’s an example of a siloed situation: Customer “A” gets a coffee every morning at the local fast-food restaurant. A potential offer would be to send customer “A” a free cup of coffee now and then to keep them coming back. On the other hand, here is an example from a data-enriched environment. “A” buys their coffee using their smartwatch. What special offer would upsell that customer best? A coupon for a half-price donut with the morning coffee or a $1-off voucher for a healthy, low-calorie fruit drink? Third-party market research might say smartwatch customers tend to be calorie-conscious. Stitching the data from the payment mode, market research, and customer regular footfall, marketers now would try to upsell Customer “A” with a healthy snack or breakfast offer. “Cross-pollinated” coffee purchase data result in more enticing and effective offers to the customer segment, increasing both revenue and customer satisfaction. Looking for data trends and connecting the dots helps companies achieve their missions and goals.

Ask questions of the data! Do customers make a different decision if they use their home computers versus mobile phones? Is debit or credit card spending behavior information relevant? Siloed data will not give the full picture. But we must also ask whether this level of customer data collection and use is ethical and legal, which leads to data governance.

3. Pay attention to data governance. Data governance boils down to the correct use of data-centered around security and trust. Data are used “properly” according to the Data Governance Institute when it is clear about “who can take what actions with what information, and when, under what circumstances, using what methods.” Organizations can choose to make their own data governance rules, or they can have them imposed by outside organizations. It’s best to adhere to a company’s own policies, so it is part of its culture. Adhering to data compliance and governance rules is essential for companies that are embarking on digital transformation initiatives. In the same way that traffic rules help consumers reach their destinations safely, data governance helps organizations ensure the security, accessibility, and proper use of the data.

For companies in catch up mode

Companies are at various stages in their digital transformation process. For those companies that want to keep up with the digital age and operate in the data economy, it is important to understand these principles.

1. If companies try to back into the data, they will fail. They cannot be a genuinely data-centric company if they use the data to validate what they already thought. They need to let the data tell them what to do. It requires patience and having trust in the people with the skills to analyze the data.

2. Make better use of the data available. Companies that squeeze the juice out of their existing data without violating increasingly onerous privacy laws are not collecting new data but are making better use of the data they have. Then they take the personal information out of the raw data (e.g., age, ethnicity, address, income) and make decisions on a person type who fits that description creating a customer profile, but not that individual person.

3. Be true to the company’s vision and mission. Trying to be an Amazon or a Google won’t help. They’re in a different lane. Companies that provide products and services to solve society’s real-world problems have a strong brand and a successful competitive advantage.


Protect profits and privacy

Data collection and sharing make headlines for a good reason. Each time a person interacts with one of the FAANG (and other digitally mature) companies, they become a piece of data. But that’s all they need to be. Their age, location in the world, ethnicity, and buying habits do not have to be tied to them personally. In the aggregate, however, a group of people’s buying, eating, entertainment, travel habits, etc., can prove extremely effective in helping entities including companies, governments, and schools get the best return on their technology, services, marketing, and production or education investments.

Those in leadership positions can argue that better use of data could result in a superior use of a country’s natural and workforce resources, increased productivity, and less global waste. Additional benefits can include more targeted medical care and more—not less—privacy as data scientists and data analysts with the proper technology leverage data efficiently and effectively in line with organizations’ missions and visions. These resources can work in a culture that cares about the public good while also protecting profits and privacy.

Improper use of data can undermine trust in all our systems. Players of all sizes in the data economy game can choose to use data for good.


About the Author:

Mitesh AthwaniMitesh Athwani is a senior IT leader with over 15 years of experience in services, consulting, analytics, and data governance. He has an MBA and a degree in Computer Science Engineering. For additional information, please connect on LinkedIn or contact