Right-sizing your Analytics-driven Insight Strategy

"How to design and deploy an Analytics-driven Insight strategy that is right for your business."


I have spent almost 20 years in data, analytics and insights. In that time, I have built practices (often from the ground-up) in top-tier consulting, global & multi-national organisations, in global private equity-owned organisations, and now my own business. I have been through the cycle of starting, growing, maturing, and operating data, analytics & insights practices in many different organisations and have built and deployed strategies for other organisations.

In this series of articles, I will provide a high-level introduction to a method I have used to build successful data, analytics and insights strategies and programs in different organisations over the years.


Article #1: Achieving Strategic Alignment

Article #2: Right-sizing People & Capability

Article #3: Right-sizing your Data Program

Article #4: Right-sizing your Technology

Article #5: Right-sizing Operating Model

Article #6: Your Horizons of Growth

Article #7: Operating a High-Performance Team


In this article, we provide a deep-dive into the Phase of achieving Strategic Alignment

 

Right-sizing your Analytics-driven Insight Strategy


Data & Analytics driven Insights programs can be transformational and can drive significant step-change, however achieving success through this type of transformation is difficult. Integrating and scaling analytics into a business is a complex, multidisciplinary, cross-domain undertaking and many businesses that have embarked on this journey have had underwhelming experiences.


In my experiences, I have observed a few factors that contribute to these challenges,

The first is that our approach to adopting analytics often focuses too much on technology and too little on the actual needs of the business. This includes failing to align initiatives with the strategic priorities, principle risks and competitive threats of the business, as well as insufficiently understanding the data & technical literacy of the intended users. We also often overestimate the businesses aptitude & appetite for change. In all of these cases, we tend to be technology-led in the hope of business adoption. History tells us that the concept of "Build it and they will come" only really works in the movies!


The second major challenge is that I believe that Analytics & Insights are often misplaced within the business. As stated above, Insights programs of work should be driven out of the businesses strategic objectives. To achieve that, the Insights function should be tightly coupled with the strategic functions of the business and then enabled/supported by the technical functions. We need to be business-led and technology-enabled.


"When pursing value creation through data & analytics, we need to be business led and technology enabled, and not technology-led in the hope of business adoption."

The third is that in many cases the execution of analytics-driven insight initiatives end-to-end in a commercial organisation is very poor. Even if we do have an initiative with strong strategic alignment, the ability of the team to achieve sustainable deployment of the models, integrate outputs into business processes and decision-chains and to facilitate change management beyond the model is often not achieved. I cover end-to-end value creation in detail in my article on the Insights Value Chain.


One of the fundamental reasons I believe that we have these challenges is that we don't construct our Analytics & Insights strategies effectively and we don't Establish, Enable and Embed our Analytic & Insight programs effectively into a lot of organisations.


It took me many years to get my head around how to do this in a commercial organisation and In this series of articles, I share my view on two core areas of Analytics & Insights strategy development:

  1. How to develop an Analytics-driven Insights Strategy

  2. How to deploy that strategy across three horizons of growth in what will become your Insights program


How to develop your Analytics-driven Insights Strategy

Achieving real value creation through Analytics-driven Insight starts with right-sizing your strategy. To right-size your strategy I outline a series of Alignment phases including:

  • Strategic Alignment

  • Functional Alignment

  • Operational Alignment

  • Strategic Deployment



In this article, we will deep dive into Strategic Alignment and provide a high-level overview of the other Phases. We will provide Articles in the future that deep dive into each of the areas to provide a sufficient guidance on how to complete each phase in your business.


Achieving Strategic Alignment


In the Strategic Alignment phase, we initially start by understanding the Vision & aspiration for Analytics & Insights within the business. This is the high-level view of what the leadership of the business aspires to achieve from this strategy over the next few years.


We then move into developing an understanding of the strategic and commercial intent of the business. i.e., "What is the business trying to do and how is the business trying to create value."


Mapping Strategic Intent

"Understanding the objectives and goals of the business"


Strategic intent should be considered as the sequence of strategies that define how a business intends to achieve its overall objectives & goals within a defined time horizon. Strategic intent could be related to driving Strategic Growth, managing Principle Risks or combatting competitive Threats.


One of the challenges with understanding Strategic Intent is that Organisational Strategies are often defined at "50,000 feet" and it can be very difficult to understand how these strategies would be practically actioned within the existing business. To curate initiatives that will make an impact we need to be able to reduce macro-level strategic thinking to a level of actionability.


An effective way I have found to frame the strategic intent of the business is to use the Objectives, Goals, Strategies, and Measures (OGSM framework) framework in a way I call OGSM Trees (if you would like to know more about OGSM Trees contact me directly). In OGSM Trees we decompose Enterprise level Objectives down through a series of Strategic levels to get to a point where we understand who in the business will tactically & operationally execute.


To map Strategic Intent using an OGSM Tree, we start with an Enterprise Level Objective. We can often find these in the Annual Report, Strategic Plan or through talking to executive stakeholders. We then reduce that Enterprise level Objective down to a level of actionability. As an example, we would first start with a macro-level Objective & Goal:


  • Objective: Be the worlds leading supplier of ACME widgets with a [Goal: Annual EBITDA growth of 5%], Owned by the CEO achieved by the end of 2020.


We would then look to reduce that Objective & Goal down through a series of Strategies and Measures in a hierarchical way (logic tree) to understand how the business plans to achieve that Objective & Goal i.e.:

  • Strategy 1: Drive a Global increase in annual sales revenue of [Measure: 10%] by 2020, Owned by Global Sales Director achieved by the end of 2020, to achieve that we will...

  • Strategy 1.1: Drive revenue growth in the Asia Pacific region of [Measure: 10%] by 2020, Owned by Asia-Pacific Regional Director achieved by the end of 2020, to achieve that we will...

  • Strategy 1.1.1: Drive revenue growth in Asia-pacific for B2B Product X in 2020 by [Measure 15%], Owned by Product X Product Manager and achieved by the end of 2020


As we move through each layer we understand who is the sponsor, who will fund the initiative, who is accountable for tactical execution, what the estimated timeframes are and what the measures of success need to be.

This is an easy process to iterate through with the business and gives us a great understanding of strategic intent down to a level of actionability.


It is at this point that we now start to map our understanding of "actionable strategic intent" into Insight Value Propositions.


Value Framing

"Translating business objectives to the analytic & insight space in commercial terms"


To frame Insight Value Propositions in a way that makes commercial sense to the business I use a commercial value frame. The commercial value frame is something I have built out over many years and is another form of a logic tree that allows me to further deconstruct strategic intent through a hierarchy of domains to form Insight based value propositions.

The diagram below shows an overview of a single chain in the commercial value frame. The framework includes hundreds of these defined chains (contact me directly if you would like to know more) and aligned initiatives for consideration.



To continue the same example from above, let take Strategy 1.1.1 to "drive revenue growth of 5% in Asia-pacific for B2B Product X in 2020". Applying this to the commercial value frame:


  • First, we ask what is the Executive Intent of Strategy 1.1.1? Is it to drive EBITDA, enhance the valuation of the company or to support sustainability (note: principle risks are captured in the sustainability domain)? Strategy 1.1.1 is focused on driving EBITDA so we follow that chain of the tree.

  • Next, we ask how is Strategy 1.1.1 looking to create value? Strategy 1.1.1 is focused on driving EBITDA through enhancing Revenue.

  • Next, we ask how is Strategy 1.1.1 looking to drive (Value Driver) Revenue? Strategy 1.1.1 could look at increasing Revenue by gaining a greater share of the market

  • Next, we ask how is Strategy 1.1.1 going to gain greater market share? Strategy 1.1.1 could focus on a range of areas including increasing the Usage and Value of the existing customer base

  • Next, we start to consider the types of data & analytic driven approaches that could drive Usage and Value with the existing Customer base. I.e. Price Optimisation.

  • Next, we could formulate an Analytics-driven Insight Value Proposition around Price Optimisation of Product X in the Asia Pacific market.


IMPORTANT: People are some times confused as to why we map Strategic Intent and then go through commercial value framing. Strategic Intent is focused purely on understanding what the business is trying to achieve, we make no attempt to view that as an Analytics problem. Value framing is designed to map that business problem to the Analytic & Insight space in a way that makes commercial sense.


After mapping strategic intent and commercial value framing, what we now have is a chain that maps from our Initiative all the way back to the CEO and the Enterprise Objectives as well as a detailed understanding of the required stakeholders, measures and time frames.




Hopefully, you can see through this that when we frame and present our initiatives in this way it is very clear to executive leadership and the business how the Insight Initiative will create value in the business and who will need to be involved in the initiative across the business.


Prioritizing Initiatives


At this stage of developing the Insights strategy, we have mapped strategic intent and undertaken commercial value framing and defined a portfolio insight Initiatives that are directly aligned to the Objectives and Goals of the business. Having a large pool of strategically aligned initiatives is a great thing; however, we need to be able to prioritise them effectively.


I use a Value v Cost, Complexity & Risk framework. At this stage, I sit with executives across the business and each value proposition & initiative is estimated, evaluated and plotted on a region in the following canvas.



Note: The dotted line you see on the diagram is a "line of material value", this is the point at which a value creation becomes important to the business. This is different for every business. Whilst the Diagram says EBITDA - Value does not always have to be EBITDA

Note: At this point, I have usually formed an understanding of the current maturity of people, data, technology, and operating models deployed in the business. Assessing complexity is dependant on understanding the current level of maturity across these domains.

Through plotting Value Propositions on this canvas, we can start to understand where we should be placing our focus. Our core considerations are 1) what can we do now to accelerate value creation (Quick Wins) and 2) what propositions are going to be more challenging but have a more significant potential return (Strategic Projects).


We also want to identify propositions that are highly innovative and likely to be transformational for your business but have higher uncertainty of a successful outcome, i.e., cost, complexity, and risk (Research & Development). Many "new product & innovation" initiatives fall into this category.

We also want to identify the propositions where the value v cost complexity & risk are not appropriately balanced (Avoid, Consider Carefully). I tend to put any Initiatives where the value proposition can't be clearly articulate into these regions.

Business-as-Usual initiatives currently have the people, process and technology in place to deliver and do not require augmented skills, technologies or investment to deliver - the business should just do them. These initiatives help define strategies for uplifting data and analytics capabilities of general users across the business.



Curating Value-Creating Initiatives

"Turn Value Propositions into Initiatives"


At this stage, I look to undertake a deep dive of any Value Proposition that falls in the Quick-Win, Strategic Project or R&D space. This is a process that does the following:


  • Develops Arguments for each value proposition - "why cant the business do this now?" I.e. Why cant the business optimise the price of Product X in Asia-Pacific now?

  • Determines the level of factual level of support (Premise) for the Argument - is that a fact, a belief or a guess?

  • Where we have unsupported arguments we reach a Juncture - this is a point where we perform target analysis to validate the Argument and the Value proposition. We need to ensure that our Value propositions are factually supported and are not just the voice of the HIPPO (Highest Paid Persons Opinion)

  • When we have supported arguments we develop Value Hypotheses - IF we do [ACTION] THEN [OUTCOME] CREATING VALUE OF [MEASURE]

  • We are then able to translate Value Hypotheses to detailed Value-Creating Initiatives


Note: I will be publishing an article on this process in more detail in the coming months


Classify & Tag Initiatives


One of the key things I also do at this stage is to classify the initiatives at 3 levels as either an Insight, a Product or an Automation.


An Insight initiative produces an output that is consumed by internal stakeholders to support decision-making for the purpose of driving business outcomes. These follow Insight Management Methodology and seek to achieve Insight-to-Business fit.


An Automate initiative leverages AI and analytic based method to automate tasks within businesses to achieve process efficiency and improved business outcomes.


A Product initiative leverages analytics as a core component to develop new products or features (as the output) that are consumed directly by an external customer. These typically follow Product Strategies and seek Product-to-Market fit


Note: Building a Product is a very different process and requires augmented skillsets to support a successful go-to-market.

I also look to Tag the level of analytical complexity required in each initiative. Tags include:


  • Descriptive Insight

  • Diagnostic Insight

  • Predictive Insight

  • Prescriptive Insight

  • AI & Cognitive Technology: Vision, Communicate, Sense

  • Automate: Robot Process Automation


IMPORTANT: This process of classification and tagging are critically important in the Functional Alignment phases as they help us calibrate the People Capabilities, Data & Technologies that our program will require to be successful.


Timing Initiatives