Establishing a Framework for Data & Analytics Success

An organization’s data is a valuable asset that can be mined to drive improved decision-making and operational efficiencies and, even more importantly, commercialized to generate incremental revenue. However, many companies struggle to achieve these goals because they don’t leverage a holistic framework to define their optimal future state, create a roadmap to build towards this state, and continually reevaluate these plans so they can adjust to new learnings and changes in the dynamics of the business and its customers.

The specific data & analytics needs of each company vary significantly based on the type of business they are in, the potential ROI of investing in data & analytics capabilities, the current state of these capabilities, and a range of other factors. However, there is a common 3-part framework that can be used to help meet any company's needs and ensure they are on the right path in terms of maximizing the economic value of the data generated by their business. Before diving into the framework, there are a few important things to keep in mind.

• Data & analytics is a business function and not a project. It cannot be treated as a discrete initiative that is worked on and is then “done” as business needs will evolve and data & analytics capabilities will need to evolve with them or become obsolete
• The underlying technology of data & analytics is just as important as the exciting data science and machine learning applications. Without robust, flexible, scalable technology to capture raw data and transform it into consistently accurate business information, your efforts will at best have limited success and at worst lead to wrong or untimely decisions
• Also over-invest in ensuring the quality of your data – garbage in = garbage out
• Establish a process by which you regularly leverage the framework to reassess your current state and adjust your future needs and roadmap accordingly. Business operations, customer needs, and market dynamics are constantly evolving, and the roadmap needs to evolve to ensure resources are being invested in the most impactful opportunities

FRAMEWORK FOR DATA & ANALYTICS SUCCESS

1.    Current State Assessment

• Commercialization & Other Outcomes: To what degree are data and analytics being leveraged to drive increased revenue or reduced cost within the company’s existing business? Where, if anywhere, has the business attempted to generate revenue through productizing and selling data? What other outcomes is the company currently attempting to drive using data and analytics? How successful have all these efforts been? What is working / not working well and why?
• Data Capture: What data is being generated by the business today? What is the quality of this data capture (e.g., are people consistently using the same naming conventions, collecting relevant metadata in a standardized format)?
• Data Platforms: What, if any platforms have been set up to store data in a centralized location(s) for analytics purposes? How flexible/scalable/cost effective are these platforms? How are data pipelines built to bring the data into the platforms? How well is data structured in these platforms? How is the quality of the data in these platforms being ensured so that the business is using accurate information to drive decisions and commercialization opportunities? How is data being governed to ensure proper access controls, etc.? What security protocols are in place to protect the organization’s data?
• Analysis: How is data being utilized to generate analyses? If no underlying data platform, how is the business collating and presenting this information? What reports, dashboards, and other self-service analytics tools are currently in place and are there opportunities to improve the value of these solutions? What level of data science / machine learning is being leveraged and how well is this set up to enable future performance?
• System Interfaces: How is data being leveraged across different business systems (e.g., customer data shared across sales and marketing technologies)? How is accuracy, completeness, security of this data being ensured? Where are there gaps in this?
• People: What in-house and outsourced data and analytics resourcing is in place? How are these resources being leveraged and what is their overall skill level?

2.    Future State Definition

• Data & Analytics Vision: What are the overarching vision and guiding principles for how the company wants to leverage and commercialize data and analytics?
• Commercialization Opportunities & Other Outcomes: What opportunities exist to leverage data and analytics to drive increased revenue or reduced cost within the existing business? What opportunities exist to productize and sell data to generate net new revenue streams? What other outcomes are the business and key functions looking to achieve via data & analytics? What is the ROI on pursuing these efforts? How should they be prioritized? Which are achievable given willingness to invest?
• Required Capabilities: What needs to be put in place technically and operationally to achieve these opportunities / outcomes given learnings from the current state assessment? From a tech perspective this would include platform technology, data pipelines, additional data capture, data quality monitoring, security controls, etc.? From an operational perspective this could range from process changes to, for data productization opportunities, new sales and marketing capabilities
• Investment: What level of investment is the business willing to make to achieve the commercialization opportunities and other outcomes?
• Organization Design: How many FTEs - internal and/or outsourced - are needed long-term to achieve the data and analytics vision within the defined level of investment? Where should these resources sit in the organization?

3.    Roadmap

• Commercialization Opportunity & Outcome Sequencing: What is the planned timing for delivering the desired opportunities / outcomes and what is considered in scope for each phase of delivering them? What quick wins can be achieved along the way?
• Capabilities Buildout Plan: What is the sequence of technological capabilities that need to be put in place to deliver the above opportunities / outcomes within the planned timeline? What operational capabilities need to be in place by what time to enable this?
• Organization Buildout: What resources are needed and when to deliver and then operate these capabilities? How can consultants and lower cost offshore / outsourced resources be leveraged to address internal resourcing/expertise gaps during the build and then for ongoing operations?

Hopefully this framework helps you think through all the questions that need to be answered to establish and continually reassess / evolve a successful data & analytics roadmap. Please reach out if you would like to discuss in more detail as I am passionate about this topic and happy to share the learnings from my past successes and failures in building world-class data & analytics capabilities.