How effective diagnostics generate value for business and pave the way for successful data initiatives

Good data initiatives support specific business objectives: how a retailer might use sales data to inform how to prioritise shelf space or how a SaaS company might use insight on its customer behaviour to inform cross-sell tactics. However, there are inevitably moments where a manager or leader is unsure where to start, particularly if the organisation in question is in the early stages of its data maturity. The issue is compounded when the same leader must justify investment into a particular initiative – how do you compare one opportunity against another let alone create a compelling business case to support it?

These are the questions the analytics diagnostic was designed to answer. The first goal of the tool is to provide a detailed list of value-creating opportunities that are tailored to your organisation’s data and capabilities, prioritised by the criteria most important to your stakeholders whether that’s the cost of the investment or the complexity of a successful project delivery.

From QuantSpark’s experience these opportunities are typically pragmatic:

• Achieving 10% increased productivity by automating manual processes such as management reporting

• Reducing churn by 5% through analysis of financial data and customer behavioural patterns

• Increasing sales revenue by 20% through effective lead scoring

Even when you are certain of the opportunity you wish to tackle and have sized the potential return on investment, there is still the matter of how best to implement the project. Should a consultancy like QuantSpark drive the project or should it be in-housed? How can you ensure the long-term sustainability of the project and avoid it becoming a desk exercise?

The second goal of the diagnostic therefore is to present an effective roadmap for implementation of any given initiative, taking into account not only practical details but the cultural considerations: thinking how best to win further buy-in from sponsors and ensure adoption by end users. The result is a well-designed plan that demonstrates financial value early on and sets out how to embed the project within your organisation.

How does the process work?

Our teams approach the process through two lenses:

Business strategy: what are the organisation’s goals and how can they be supported and delivered through analytics?

Data capabilities: how strong are existing analytics and engineering capabilities? How can they be enhanced further?

Whilst data initiatives instinctively feel technical, there is an important cultural assessment to be made to understand how employees and end users will interact with future projects. How can the project sponsors best communicate why the advantages of their initiatives? Assessing this upfront will call out future barriers to success.

The diagnostic itself is carried out across three sprints that are designed to be scaled according to the size of the organisation. QuantSpark has conducted diagnostics that range from 2-3 week engagements with SMBs to month-long projects with multi-billion dollar companies. Efficiency is key – the diagnostic is about enabling future projects and its value lies in identifying risks and opportunities up front.

Sprint 1: Strategic workshops

The first sprint is centred on the business’s strategic direction and whether there is a modern culture of data-driven decision making. Through workshops we dig deep to understand what makes the company’s products valuable to their customers, strategic goals and challenges. We also use this sprint to understand the company’s organisational structure and the impact of any previous analytics projects. This initial step is often integral to highlighting key business objectives and how analytics might support their delivery, assessing the potential for high commercial impact.  

Sprint 2: Data workshops

The second sprint audits hard skills such as the business’s engineering capability, data architecture, KPIs and reporting quality. We work with your data and IT teams to assess the quality and availability of data, benchmarking against suitable industry standards. At each stage we tie our understanding to a potential opportunity, considering which will present the highest value to our client – questions that can usually be answered with an effective pareto analysis. This is a simple but crucial step to inform a pilot scheme.

For example, if a client is most interested in understanding how best to reduce customer churn, we focus our efforts on identifying where churn reduction to specific customer segments would make the biggest difference to the bottom line.

Sprint 3: Recommendations & roadmap

The final sprint brings all our findings together, setting out a vision of high value, data-driven opportunities and then working backwards to provide recommendations of technical and practical improvements that bridge the gap between current and future states. Those opportunities are ranked by:

• Commercial impact: In short, which initiative will make the biggest difference to your business?

• Speed of impact: How quickly can the project be completed?

• Data availability: Is there sufficient data to provide the required level of insight or will we first need to facilitate acquiring more?

• Pilot feasibility: Is there a clear way of testing the opportunity as a proof of concept in order to demonstrate value and pave the way for further buy-in?

• Risk factor: What is the cost of doing nothing vs the risk to the business of this project going wrong? Is there a chance it can cause more harm than good?

• Wow factor: Which opportunity will most impress your board and stakeholders?

We ascribe a numerical score to each in order to make an overall recommendation about where to focus business resources that can form the heart of a business case for the project. Our findings are delivered in three key reports:

• Data health report: A comprehensive assessment of current data practices and – through industry benchmarking – identify strengths and opportunities.

• Opportunity matrix: A menu of fully costed analytics projects. We evaluate each opportunity for feasibility, impact, speed to market and return on investment (ROI). We only recommend projects with a clear positive ROI business case.

• Data strategy roadmap: A timeline and methodology for delivering the chosen opportunity.

Using diagnostics effectively to return value

The guiding objective behind any diagnostic is to help our clients unearth data-driven initiatives to either improve their business or make more money, and to develop an empirical business case for delivering those initiatives. The best diagnostics connect each initiative to your organisation’s objectives, answering the key questions of why, how, and how much? As with any well-thought out strategic plan, they are a crucial step for assessing up-front where value lies, what objectives or risks must be overcome, and how to implement in a cost-effective manner.