Co-created Data & Analytics Strategy — The Missing Link to Business Success
Authors: Keyur Desai (ex-Chief Data Officer, TDAmeritrade) and Jay Zaidi (Managing Partner, AlyData)
Keywords: Digital Transformation, Corporate Strategy, Corporate Data & Analytics Strategy, Data Management Maturity (DMM), Data Governance, Data Quality, Data Privacy, Data Vocabulary, Artificial Intelligence, Machine Learning, Business Outcomes, Business Analytics
The pandemic has accelerated digital transformation efforts within most organizations. High quality data and robust analytics can accelerate an organization’s digital transformation journey. According to a Gartner article titled “Why data and analytics are key to Digital Transformation”, fewer than 50% of documented corporate strategies mention data and analytics as fundamental components for delivering enterprise value. Gartner predicts that this will change quickly. By 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.
The lack of a clear corporate data and analytics strategy or misalignment between the corporate strategy and the corporate data and analytics strategy risks a corporation’s ability to achieve its business outcomes and impacts the future viability of the corporation.
The corporate data and analytics framework that we share in this article incorporates patterns and insights we’ve gained over the last five years across multiple client engagements in financial services, federal government and hospitality verticals.
Components of a good Data and Analytics Strategy
Data and Analytics Strategy is a very broadly used phrase so it’s important that we align around what it means. We must embrace the notion that the combination of high quality data and deep insights generated from analytics have the ability to affect business outcomes.
The diagram below depicts the components of a good data and analytics strategy — starting with a clear definition of the desired business outcomes.
But several questions arise:
- What specific data and analytics initiatives need to be created to achieve the desired business outcomes?
- What should our technology architecture be and how does it enable our business outcomes?
- What sequence of data and analytics initiatives deliver the desired sequence of business outcomes? and
- How does the cost of our data systems relate to the benefits to our business?
A Data and Analytics Strategy answers these questions, and in so doing connects business outcomes with the data and analytics initiatives needed to achieve them.
The Data and Analytics Strategy must be co-created by the business and data and analytics teams jointly.
There are two attributes of a Data & Analytic Strategy that are important to know and embrace. First, given the connection we spoke of earlier, between Data and Analytics and its ability to affect business outcomes, it follows that the Data and Analytics Strategy must be co-created by the business and data and analytics teams. Secondly, since business needs and goals are continually evolving, so too will the Data and Analytics Strategy. This means that after base lining the initial Data and Analytics Strategy, business and data and analytics teams need to come together on a regular basis to ensure that the connections between the desired business outcomes and data and analytics initiatives are always aligned and accretive to the firm.
Specifically, a comprehensive data and analytics strategy must contain the following:
- Prioritized listing of specific, ideally quantified, business outcomes being addressed, an example being increase revenue by 5%.
- Data and Analytics challenges. An example of which are “Need Access to Current and Trusted Customer demographic data” or “Inability of business users to formulate insights in a timely fashion”.
- Current-state environment assessment and desired future state; both used to determine gaps that need to be surmounted to reach the desired target state to deliver business outcomes.
- Define policies, coherent actions and guiding principles.
- Prioritized execution roadmap and plan of data and analytics initiatives and accompanying business initiatives to reach target state, and
- Perform the value analysis to determine anticipated value against anticipated costs. This analysis becomes the basis by which multiple initiatives can be effectively prioritized without the typical prioritize the loudest.
Data and Analytics Strategy Framework
In order to understand the gaps between current state and target state we need a framework to help us determine the appropriate components and actions, followed by the cost and tangible benefits that the organization will accrue. Given that data and analytics have the ability to affect business outcomes, it is important when forming a data and analytics strategy to know what the sea of all currently possible types of business outcomes with Data/Analytics are? Some examples are: process optimization, better human capabilities, better competitive position, new or better products and risk mitigation.
You are much better off focusing on revenue based initiatives as they provide a more sustainable competitive strength over a cost initiative, which typically can be easily replicated by competitors. Per McKinsey statistics: on average, a company can increase margins by 6% by achieving these business outcomes with each revenue initiative delivering a 1–2% increase in revenue with 0.5% to 1% increase in margin and each cost initiative delivering 10–50% decrease in cost. It takes approximately 3 years to see these results as it takes time for the culture and technology changes to diffuse across the organization. The impact is not small by any means.
AlyData developed a business outcomes value chain that provides a formula for the generation of the desired business outcomes. This value chain contains technical, business and operational components. Within the technical component lie the (1) Data, (2) Analytics, (3) Technology capabilities, while the business component has the (4) People and (5) Process capabilities, and the operational component contains the (6) Data Operations capability.
It is important to recognize that these six capabilities are linked together to form a chain. Strength in all six capabilities — Data, Analytics, Technology, People, Process and Operations is needed to achieve the desired business outcomes.
To have strength in Data, data consumers must be able to find the right sources at the right frequency, right latency, right history, right breadth, and then link them together, enriching the data and bringing quality to it.
To have strength in Analytics, organizations must be able to create the right type of analytical capability on top of the data to meet the desired business outcome. They may wish to understand what happened and create a descriptive analytics capability, or wish to predict an outcome and create a predictive capability or they may wish to know the actions to take to make the predictions happen via a prescriptive capability like Artificial Intelligence or Machine Learning, etc.
To have strength in Technology, organizations need to have the ability to create the right technology foundation that allows them to ingest and store the volume of data needed at the latency it is coming in at, gracefully. This also means being able to store the types of data that may be required to achieve the business outcomes like images, voice, social media as well as character and numerical data. They need processing capabilities that allow them to process the volumes of data at the performance levels expected for the business outcomes, and the concurrency of requests expected now and in the future.
To have strength in Operations organizations need to ensure they can deploy new functionality continuously and rapidly, proactively identify and correct data quality, privacy, and access control issues and ensure predictable performance across all aspects of the technical stack.
To have strength in People, organizations need to have the deliberate ability on the business side to be able to take the analytical output and convert it into insights, and from there be able to make an action plan, then to implement the action plan, and to also mount the cultural change necessary to rally the targeted teams behind the action plan.
To have strength in Process, organizations need the deliberate ability to take the technology foundation, take what the insights and action plans are telling them and then commensurately scale business processes. For example, this may mean they automate some business processes; like a manual account opening process becomes automated through workflow software working cooperatively with the technology foundation. It may mean they adapt some business processes where they previously had representatives in the call center handle a churning customer with little effect - they decide to create a specialist desk or churn desk to prevent churn more effectively. They can make some business processes more agile with easier access to clean data.
The chain is only as strong as the weakest link on the chain. So the organization’s ability to create the desired business outcomes is only as strong as the weakest capability.
An organization can be best in class in five out of six capabilities, but if it is weak or has low maturity in even one capability, its business outcome is weak. Hence, there is a multiplicative relationship between the six capabilities. What this means is that organizations must be strong in all six capabilities to achieve our business outcome.
An organization can be best in class in five out of six capabilities, but if it is weak or very limited in even one capability, the business outcomes are weak or not effective.
4-Step Execution Methodology
In order to successfully deliver against a well crafted data and analytics strategy, organizations must have strong execution capabilities. We have developed a 4-step methodology to do this. You start with the highest priority tactical and strategic business outcomes, develop the business outcomes value chain, followed by an execution roadmap and program plan and then execute against the roadmap and plan.
Every organization must ensure that it’s corporate strategy should include data and analytics as fundamental components for delivering business outcomes. The lack of a clear corporate data and analytics strategy or misalignment between the corporate strategy and the corporate data and analytics strategy risks a corporation’s ability to achieve its business outcomes and impacts the future viability of the corporation.
High quality data and robust analytics capabilities can accelerate an organization’s digital transformation efforts. Organizations can utilize the strategy components, data and analytics strategy framework and the 4-step execution methodology we’ve described in this article to develop their co-created corporate data and analytics strategy to achieve the desired business outcomes.
In closing, we’d like to recap the four major contributions we’ve made to the data and analytics strategy discussion -
First - A Data and Analytics strategy must be co-created with the business partners — to ensure that it aligns with and supports the business strategy. After base lining the initial Data and Analytics Strategy, business and data and analytics teams need to come together on a regular basis to ensure that the connections between the desired business outcomes and data and analytics initiatives are always aligned and accretive to the organization.
Second — Each of the six capabilities of the business outcomes value chain must to strong to generate the desired business outcomes.
Third — Organizations will have to introduce a new role — that of a Data Economist to develop the value proposition and the return on investment metrics.
And finally — cross-functional engagement is required to drive the data and analytics strategy and ultimately a data-driven culture.
About the Authors:
Keyur Desai is the former Chief Data Officer for TDAmeritrade. With 30 years of expertise in Global Enterprise Data Management, Data Monetization and Analytics Mr. Desai has consistently maximized the impact of data, analytics and data products on business results, operational efficiency, enterprise risk and innovation.
At TDAmeritrade, he was accountable for generating out sized business outcomes for the firm, using data and analytics. With a globally dispersed team, he led Enterprise Data Strategy, Enterprise Data Governance, Master Data Management, Data Warehousing and Business Intelligence, Self Service Data Lake Platform, Artificial Intelligence/Machine Learning and Data Science. With data being a Board topic, he provided Risk and Audit Committee updates on data risk. Early in his career, Mr. Desai was on the founding Team of MicroStrategy, Inc. a pioneer in business intelligence.
Mr. Desai is the recipient of numerous awards for data and analytics leadership and innovation and serves on the Board of Advisors of Springbuk, a healthcare analytics company, and Families without Borders, a non-profit organization that focuses on economic empowerment and education of youth in African countries.
Jay is the Founder and Managing Partner of AlyData — a consulting firm specializing in all things DATA. Jay and his team help CXOs drive outsized business outcomes from their data assets — through AlyData’s CDO Advisory, Data Governance, Data Risk Management, and Insights consulting services. AlyData has developed proprietary frameworks and methodologies and utilities for data risk management, data governance, and data quality — which enable it to deliver high-quality solutions faster. To learn more about AlyData visit http://www.alydata.com. Jay can be reached at firstname.lastname@example.org.