Data transforms our world and how we interact, shop and work at an unprecedented rate. Data is already revolutionizing how companies operate, and it will become increasingly critical to organizations in the coming years. Those companies that view data as a strategic asset are the ones that will survive and flourish.

Why do we need a data strategy?

In today’s world, every organization has petabytes of data. But to grow a business, you fundamentally need to change the value proposition for your customer, using data as a metaphor. 

Organizations need to get more competent at understanding what products can be enhanced and what investment in “data and analytics” drives those outcomes. In today’s world, progressive leaders are shifting the paradigm away from tools and technology and toward customer journey optimization as a business competency. This progression will take time to achieve, but D&A Leaders are in the best place to help orchestrate and lead this change.

Data can significantly boost the value of an enterprise, but so can a company’s capability to extract value from data. Data is precious when combined with sophisticated strategy, apps, and algorithms to extract vital insights from data. For example, pizza delivery company Domino captures a lot of customer data and uses that data to improve its marketing. Having solid data systems like this in place, and having the skill to work with data, makes the corporation more valuable and attractive. The value of Domino’s is more than the company that doesn’t know data for marketing as Domino’s outsmarts competitors with the use of data. 

When I was in 12th, if you wanted to pursue engineering, math was the foundation. Therefore, we had to learn math essential if we wanted to pursue engineering. In the current era, sustainable impactful data strategy is the mandatory foundation for the organization for decision intelligence, driving customer value propositions. 

Let’s understand the parameters that define the impactful, sustainable strategy. 

Sustainable, Impactful Data Strategy 

The most critical question many strategies leaders face is how can I have an impactful, sustainable strategy that drives the customer proposition in the long term? Let’s understand the eight important points which drive sustainable data strategy. 

1. Intelligence eye on data: Not all the data but specific data with value insights 

Collecting complete data on all customer journeys and processes is not a sign of business acumen. Entire information needs huge infra and storage costs. Most value propositions deliver results with actionable data, and data with Value. One of the practical examples was [1] DirecTV in the USA; they collected only data on people moving homes and marketed the DISHTV connection to such homes. And they have increased their business and made enormous business value. 

2. Integrated Data Analytics as a Service

The entire issue with the IT Industry, the analytical tool being used for analytics, and the specific outcome, are fixed by management as KPIs. However, analytics as a service is not developed. Analytics as a service will throw non-KPI charts for consumer behaviour analysis. 

In hindsight, Sony gave birth to the Walkman and ideally, they should have been the company who should have invented the first iPod; however, it was Apple who ran the extra mile and invented the iPod. The reason is that no one knows whether Sony possessed an integrated analytics division that may analyse customer journey and convert it to digital technology. Their analyst team missed this idea, which transformed the music industry forever. 

In India, the retail giant, Reliance, has effectively used this integrated value chain concept. When you buy 5K from Reliance retail, they will give specific vouchers for Reliance trends; when you buy trends more, they offer coupons for Reliance digital. Initially, the company increased its market share by customer acquisition by driving the value via integrated data. An integrated value chain can make a customer of one segment a customer of another brand. 

3. Data as Process Optimizer

Data as a process optimiser is a broad category. This category covers all dimensions in which data can rationalize your everyday business processes, such as bookkeeping or customer service. There are several ways data can help you decide by optimising the number of steps. 

Car insurance companies [2] are using machine learning to analyse photos from car accidents concerning injury claims. The analytics can determine if the injuries claimed are disproportionate to the vehicle damage sustained in the accident. Again, the conclusion arrived here, used to trigger a more thorough claim investigation rather than an instant rejection. This helps the insurance businesspeople to avoid huge process costs to analyse. 

4. Data Monetization 

A future-oriented strategy always provides excellent attention to data monetization. 

There are two aspects of monetizing data: one is data’s ability to increase the overall value of a company. Second is an organization’s ability to create extra value from data by developing new products using the data. 

Companies’ ability to create value by linking insights or deriving insights determines the company’s future in new product propositions. A car manufacturer can partner with insurance companies to provide data on accident % on roads and condition of roads to avoid the same. This will add enormous value to Car companies’ customers. This data is a prime example of monetizing the data. 

5. Consumer behaviour analytics 

Data insights need to act as a facilitator that produces more use of the product or online channel and increases consumer time on the product. 

Think of this scenario: how Facebook beat Orkut. However, Orkut’s technically sound team falls short in customer and behaviour analytics compared to Facebook. Facebook added new journeys and valuable features. Moreover, Orkut environment did not have a place for developers, and Facebook did most street-smart innovative techniques by opening doors for developers. So, impactful strategies need behaviour analytics. 

6. Operational efficiency: Data needs to bring value via optimizing parameters and process optimization, and performance analytics

Rolls-Royce [3] is the prime example of a manufacturer leveraging performance data to its advantage. The company manufactures passenger jet engines, and each of those engines is full of conventional sensors. These sensors check performance in real-time, measuring 40 parameters 40 times per second, including temperatures, pressures, and turbine speeds. All the stored data is simultaneously streamed back to Rolls Royce HQ, where computers scrutinize the data to verify for irregularities. This is how modern-day companies are achieving performance using data. 

7. Product innovation

Enable data insights to innovate new products. Data insights need to give a pattern where a new customer journey can be invented. 

Amazon studied consumer behaviour and understood that carrying too many books is the most significant pain in travel. The Amazon team finally brainstormed and developed Kindle. Kindle was the innovative use of data that Amazon had. Even if you consider Airbnb is using customer vacant space data to create new customers. 

8. Data Mitigating Risk and Uplifting Value  

Any data strategy always addresses the risks of data and uplifts the value. Drivers of risk stay the same universally. They are regarded as the increased risk to cybersecurity and threat of noncompliance to regulatory. The pursuit of risk mitigation is likewise associated with value uplift. Here, as you make data more secure and better encrypted, and with governance, you increase the customer trust index. The more secure and correct data is, the more companies can leverage artificial intelligence and emerging technologies to make decisions around it. 

In conclusion, one must continuously analyse if fundamental business needs have changed. Is your organisation adopting the new age analytics? Is “shift left toward business needs and shift right toward strategy modification an accommodating game that one must play all the time. There is nothing called good or bad strategy, because how much value your strategy brings for your customers and organisations matters most. The strategy should always have a context denominator of business value and product propositions.

Virendra Dafane is an IITB alumnus with 20 plus years of experience in the IT industry.

References

[1]How DIRECTV Gets Double-Digit Conversion Rate Lifts With Personalized Content – Business 2 Community Posted on 13th May 2016

[2]Insurance company uses artificial intelligence to assess car damage when claims are made (computerweekly.com) Posted on 13th March-2020

[3] Marr, B. 2015a. “How Big Data Drives Success at Rolls-Royce.” forbes.com. Posted on June 1st, 2015. Accessed on November 21st, 2015.