How to ensure a successful data strategy

Data and algorithms are increasingly driving businesses today. Acknowledging and accepting the use of data to define the business strategy is absolutely critical for business stakeholders across industries. Data gets more complex as it gets richer, so, it’s imperative that organizations prioritize their data strategy in concert with their overall business strategy.

Understanding the challenges in developing a data strategy

A full-fledged data strategy involves three critical areas,

• identification of data-led use cases across the value chain of an organization,
• determining the technical requirements to deliver the use cases, and
• assessing organizational and operations changes and improvements to capture the value of the use cases.

A key challenge is to ensure that the relevant data is captured and stored in such a way that it can be used, shared and moved easily and efficiently across the important functions of an organization. Data is no longer a by-product of business operations. It is a pivotal asset that enables smart decision-making. However, a common challenge that business stakeholders face is around delivering measurable business outcomes as a result of a data strategy. In other words, delivering on the ‘data promise’. If we don’t have a smart data strategy, these initiatives risk becoming one of the vanity projects people find interesting but stop supporting once they realize that the value is not captured by the business.

Tips for a responsible data strategy

• Focus on high-value data use cases: From manufacturing to retail to travel, every industry collects a large volume of digital assets with the aim to enhance their efficiency. Out of the vast amounts of data gathered, it is difficult for analysts and other business leaders to identify the right business use case. Once the use cases have been identified, an organization can then bring life to the data, thereby boosting organizational growth, accuracy, productivity, and efficiency. Therefore, the first step towards getting the desired business outcomes is identifying the right data use cases. For e.g Personalizing the weekly communications, a Retailer sends to its customers with the specific offers/promotions on the top 10 most relevant products based on historical purchase behavior.
• Identify the right source of data: Identifying the right sources of data is crucial. Start with in-house systems and first-party data that are already available within the organization. The next stage would be to discover ‘relevant’ sources of second and third party datasets. Second and third-party data should be put to test by asking the right questions: ‘Is the data source a reliable brand or a well-known data partner in the industry? How was this data captured and in what context?’, ‘What is the value of this data to our organization and are there non-obvious use cases that would help us break away from the competition?’ For e.g, An Insurance company had built better customer retention models by leveraging rich loan repayment behavior data sourced from a Credit bureau and shopping basket data from their retail partner. Data gaps are crucial pieces of information that are missing in the planning of strategy and execution. This includes some precious nuggets of knowledge about customers, sales prospects, the market in which brands operate, competition and internal performances. For e.g Stand-out organizations are increasingly learning more about their customers (demographics, purchases, behaviors, attributes, etc) through partnering with Data and Analytics providers that have privacy-compliant data assets.
• Technology requirements: The technology infrastructure for a data strategy assists to align with the business solution. An optimal technical solution is unique to each organization’s business needs, regulatory requirements, and future plans. Integrated data provides a complete view of the use of integrated technologies, marketers can identify valuable customer segments and apply them to strategies or deliver customized experiences. For e.g, A financial services organization had the goal of delivering a seamless digital experience for its customers through the integration of their email, website, and apps. This required a sophisticated email services and mobile push platform and Digital Experience (DX) improvements of their websites and apps, so that messaging is personalized and is consistent for each customer at scale.
• Data Governance: Data standards, procedures, and compliance are criteria that the organization must adhere to for regulatory reasons and those they voluntarily wish to adopt. These standards, policies, and models, in simple terms, define data governance. This can be a system of decision rights and accountabilities for information-related processes. This system when executed according to agreed-upon models, describe who can take what actions with what information. It also outlines the timeline, under what circumstances and what methods can assist in regulating data
• The right partner: Are marketers working with a true data expert? Any data partner needs to have a clear and accountable plan for working with an organization to improve data quality. Data is in a state of constant flux, meaning that what was true and accurate in the past - whether six months ago or six minutes ago - may not be true in the present. It is important to partner where there’s a commitment to maintaining data and bringing new innovations and products to customers.

According to a report published by Harvard Business Review, 69% state that they have not created a data-driven organization, whereas 53% feel that they are not yet treating data as a business asset.

It’s a compelling truism that’s admissible across industries: The future of business will belong to organizations that can effectively and efficiently harness and leverage their own proprietary and partner data. A clear data strategy ensures that the entire business has a specific set of data management guidelines to follow. This can then become a channel to translate the benefits of good data management to the bottom line. A successful data strategy today adds value to the business processes and leads to the journey of growth and innovation.

By Lakshmana Gnanapragasam, Vice President, Strategy and Insights, Epsilon