How a Robust Data Strategy is Key to Digital Transformation Success

Going for a long drive? You are more likely to find a McDonald’s outlet at a left-hand turn than a right-hand turn. Wondering why? Decades back, before Big Data made it big in our world, the fast food giant conducted a survey and found that their standalone outlets on left-hand turns were more profitable! Though the company was not looking specifically for this data, it did inform their asset acquisition decision for decades to come. That, in a nutshell, is the power of a good data strategy. 

According to a report by the International Data Corporation, organizations worldwide spent a whooping 3.9 trillion dollars on digital transformation projects. Shockingly, though, a staggering 70% (which 7 out of 10) of such projects might have “failed”  by missing deadlines, overshooting budgets, or not delivering the promised results. In my nearly decade-long stint of participating as well as managing large-scale transformation projects, I have realized that the single most important factor determining success (or failure) is focusing on data. 

Or the lack of the same. 

There is no underestimating the role of data in delivering successful digital transformation projects. That is what we will explore further in this article. 

Why Every Digital Transformation Needs a Data Strategy

The first step toward driving data-powered transformation is having a strong data strategy in place. Data is the figurative lifeblood of an organization that enables: 

Informed Decisions

Data empowers organizations to make data-driven choices rather than relying on intuition or guesswork. This can range from selecting the right marketing automation tools to optimizing the performance of customer support executives. 

In one of my client engagements, we chose a marketing automation platform that can support emojis and GIF’s in emails and SMS. Because the client was planning to launch a payment app targeted toward Gen Z, who respond better to quirky communication!

Customer Centricity

A deeper understanding of customer needs and behaviors can only be arrived at through data – both qualitative and quantitative. Such data-driven insight allows for the creation of personalized experiences and targeted business strategies across the board. The benefits? Increased customer satisfaction and loyalty as well as improved top and bottom lines. 

Consider designing a digital platform to sell home loans across India. Customer needs would drastically differ between borrowers in metro cities and those in Tier 2 towns. Only thorough research to understand the varying needs of the different customer segments can result in an effective platform design. 

Process Optimization

Data analysis can identify inefficiencies and bottlenecks in existing processes. By leveraging data insights, businesses can streamline operations, reduce costs, and improve overall efficiency.

One of the best examples is an age-old IVR system, which was devised to reduce the waiting time and confusion of callers making a call to a helpline. The next evolution of that was, of course, making the calling system more potent by allowing it to make automated calls! These innovations might have taken much more time if there was no supporting data to make robust business cases. 

Risk Mitigation

This is perhaps the least talked-about benefit of having a strong data foundation. Data helps identify potential risks associated with the transformation project. By proactively addressing these risks, organizations can minimize disruptions and ensure a smoother transition.

Maintaining timelines and budget – that’s the holy grail of a successful digital transformation program. The only way budget and deadline can be controlled is by monitoring them continuously. And calling out any, or even predicting, a slippage before/when it happens. A sub-optimal data strategy simply won’t cut it when it comes to nullifying risks! 

Even if all of the above is true, just having a strong data strategy to collect and use in a piecemeal approach does not translate to successful transformation projects. 

The data strategy needs to be integrated throughout the lifecycle of a digital transformation project for it to be effective. 

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How to Use Data at Different Stages of Digital Transformation?

Here’s a breakdown of how data can be effectively used across the phases of a digital transformation project. 

1. Pre-Implementation Phase

GOAL: Defining the transformation vision and goals

Answering the “Why”

Before diving into data specifics, leaders and teams need to clearly define their overall digital transformation goals. What problems are they trying to solve? What opportunities do they aim to capitalize on? Aligning business objectives with a North Star metric and an accompanying KPI-led data strategy ensures a focused and impactful approach.

Identifying Data Needs

Due diligence needs to be done on what data is needed to reach the project goals. This may involve customer data, operational data, market trends, or a combination of all three. 

Establishing a Data Governance Framework

Having clean, benchmarked, audited data is the ideal starting point. This can only be accomplished by ensuring data quality, security, and accessibility. The importance of having data that is clean and without biases can not be stressed enough. Even a small or unconscious bias in data can have a far-reaching impact on a large scale. For instance, Netflix India found out in their early days that viewing times on their app/website peak during rush office hours, but only on weekdays. A large section of their subscribers consumed the content during their commute to and from work. BUT, only on weekdays. This minute understanding helped them a lot in crafting their recommendation system. 

Hence, a strong governance framework that clearly defines data ownership, access controls, and data collection practices is critical. 

PRO Tips

• Conducting workshops with key stakeholders to understand their data needs and challenges. Having a laser focus on the transformation goals helps 
• Performing a data inventory to understand the types and formats of data currently available and identify gaps, if any 
• Developing a data dictionary to define the meaning and structure of all relevant data points. Having clearly defined benchmarks and calculation logics for derived data points (especially critical KPI’s) is an ideological state leaders and project sponsors dream of! 

2. Implementation Phase

GOAL: Building strong data acquisition and integration rails

Collecting Data

Implementing robust data collection methods ensure data accuracy and completeness during the early stages of the implementation cycle. This may involve ingesting data from various sources, including internal systems, customer interactions, and external databases. Streamlining the workflows helps reduce junk data as well as ‘noise’ 

Creating a Single Source of Truth

Breaking down the ‘data silos’ by integrating data from disparate sources into a central platform has compounding effects on the pace and eventual success of programs. A singularity not only helps in quicker data analysis and allows for a holistic view of the program, but also helps in quick decision-making. 

Maintaining data integrity & quality

Implementing data quality checks to identify and correct errors in the data ensures that insights derived from the data are reliable and actionable. While such checks are mandatory at the source of data ingestion, occasional audits also need to be performed on the data that has been processed. 

PRO Tips

• Investing in data collection tools and technologies that automate data gathering and reduce manual errors. 
• Standardizing data formats throughout the organization to ensure seamless integration and analysis. Finding a way to tackle unstructured, qualitative data is the key
• Implementing data cleansing processes to identify and rectify inaccurate or incomplete data. Care should be taken to avoid overfitting and/or overcorrection, though

ALSO READ: Why Data Compliance is Key to Successful Digital Transformations

3. Post-Implementation Phase

GOAL: Analysing data and generating insights

Leveraging Analytics Platforms

Employing data analytics tools and techniques to extract valuable insights from the collected data. This can include business intelligence (BI) tools, data warehousing, and machine learning algorithms. 

Generating Actionable Insights

‘Actionable Insights’ has long been a cliched holy grail of any data-related endeavor. Leaders and sponsors should encourage and nudge teams to generate insights that answer the “So what?” questions. 

Visualizing Data

A Forrester report has mentioned that organizations often use only 12% of the data they collect. And that’s most often because of poor visualization. Presenting data insights in a clear and concise format grabs attention and encourages quick decision-making

PRO Tips

• Developing a data analytics roadmap that aligns with the transformation’s overall business objectives. Investing in training for employees on how to interpret and utilize data insights, across the leadership as well as rank and file. 
• Creating a culture of data-driven decision-making where all levels of the organization rely on data for informed choices. Layered, top-down reporting with appropriate granularity is the optimum state of being. 

4. Continuous Improvement

GOAL: Measuring and course-correcting

Tracking Progress

Continuous tracking of the program KPI’s against their benchmark and promised measures and regular reporting of the same helps in measuring return on investment and calculating accrued benefits 

Adapting and Refining Approach

Regularly evaluating and refining the data from a completed transformation program helps organizations fine-tune their strategies and get more bang for their buck. 

In conclusion, while significant resources are poured into digital transformation projects, a data-driven approach is the key to unlocking their true potential. By implementing a comprehensive data strategy throughout the entire project lifecycle, organizations can make informed decisions, build customer centricity, optimize processes, and mitigate risks. This ensures a smoother transformation journey and unlocks the transformative power of data for long-term success.

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NOTE: The views expressed in this article are those of the author and not of Emeritus.