50+ Questions To Build Your Data Strategy Around

The use of data in today’s digital landscape can be revolutionary for businesses.

However, flicking a switch overnight and hoping for the best possible outcome won’t magically turn bytes of data into pots of gold.

In an attempt to get the gold, however, far too many organisations have been caught in a cycle of capturing as much data as possible, before they really understand what it is they want to do with it!

Instead of applying a “hoover it all up” approach, it’s important that every organisation takes stock of what they want to achieve with their data first and foremost.

In other words: you need to find your organisations WHY.

In many instances, data projects have been treated firstly as technology change programs, as opposed to business-driven initiatives.

Instead, it is important to align your use of data to strategic use cases that can be traced back towards a concrete business goal in order to generate any tangible value.

How to Find Your Why

Based on the conversations I have had with organisations, the most frequent things I tend to hear is that enterprise organisations want from their data programs is to:

• Enhance decision-making: Improve the organisation's ability to make decisions (e.g. which products appeal to customers and which do not?)

• Optimise processes: make business operations more efficient (e.g. accelerated resolution of customer complaints)

• Drive additional revenues: redefine their business model and exploit the monetisation of their data assets.

These are the headline items, which often consist of more bespoke initiatives that respond to regulatory demands, addressing customer churn, implementing dynamic pricing, or delivering tailored products and services to customers.

Needless to say, the avenues of exploration are endless!

This is why it is imperative for enterprise organisations to find their WHY, when it comes to shaping and defining a data strategy.

Based on our experiences, trying to tackle each of the three points above, at the same time, is a significant undertaking. Especially for organisations embarking on a fledgling data transformation effort, in a complex environment underpinned by a tangle of heritage and modern technology stacks (sound familiar?!).

Typically, organisations start with improving their ability to make better decisions and then move up the Richter scale by redefining their business models around data.

However, those organisations that already have a deep-rooted and mature understanding of their data sets, will often fast forward to redefining their business models as an initial starting point.

There is no secret recipe that will guarantee the successful execution of a data strategy. However, the key point here is that it must be aligned to a tangible and measurable business outcome.

Whilst those business leaders and change agents that are executing the strategy must ensure they know their own ‘why?’, it is important to the business and its customers.

Let’s expand on each of these areas in a bit more detail and provide some examples of the types of questions that firms need to ask, to fully understand the “why?” behind their respective data strategy.

Improving Your Organisation's Ability to Make Decisions

Using data to make better decisions is the common starting point for many enterprise organisations. Whether this is to better understand the market, your competitors, your customer’s buying habits, reactions to a new product or service, or a desire to increase revenue streams.

In short, data is at the heart of those insights to make those decisions.

That said, in order to make better decisions, organisations need to first ask themselves better questions to establish a launchpad for data-driven change. I’ve found that breaking down these questions into customer and financially focussed dimensions is the best starting point.

I've provided a list of questions below that your organisation should ask itself.

Customer Questions

• What are the key trends in our sector?
• Is there an increase or decrease in demand for our services and products?
• What does this trend look like based on a 5-year projection?
• What markets are fruitful and which aren’t?
• Which channels are driving the highest revenue for us?
• Who are our competitors and why?
• How do we segment our customers?
• How do we ensure our products are optimally priced?
• What does our customer satisfaction score look like?
• What are our customers saying about our products?
• Which regions and demographics are we being beaten by our competitors in?
• What are prospective customers saying about products?
• Where are we losing prospective customers in the sales process?
• What is the average length and value of a customer relationship?
• What are the key influencers for customer churn?

Financial Questions

• How does our business generate money?
• Where are we losing money?
• What are the assumptions we have about revenue and business growth?
• What are the trends and peaks of our sales cycles?
• When do we see one-off events in customer buying habits?
• Who are our most and least profitable customers?
• Where are our most and least profitable customers based?
• What does our balance sheet look like?
• How much will it cost to operate and deliver our products over the next year?
• What is market sentiment about our business?
• What does the wider economy look like?
• What are our biggest cost-saving opportunities?
• Where are their opportunities to eliminate costs from our supply chain?

As you can see based on the questions above, the formation of any data strategy needs to be aligned with key business questions and priorities.

It’s important therefore to ensure that business and technology functions are aligned and operating in unison. As a segue, I’ll explore data operating model principles in more detail over the coming weeks, so stay tuned on that front.

2. Improving Business Operations

Reducing costs to serve and efficiently managing balance sheets are major concerns for business leaders both large and small. The global pandemic has taught us that cash is king and illustrated the fragility of many organisations' financial positions. That aside, it has also accelerated the urgency across many industries to explore how business processes and everyday operations can be automated to deliver better experiences or faster responses to customers, without the need for human-intensive oversight.

Every business process is a series of events and each event captures some form of record. Whether this is a sensor on a production line, a field in a customer ordering system, or an API built into a digital customer journey. This type of data can be collated, aggregated, and analysed to identify improvements and operational efficiencies. However, merely automating existing business processes won’t deliver the maximum return on investment for organisations. Yes, automation is key. However, to understand where capabilities like enriched analytics and machine learning could be applied, firms should ask themselves;

Operations Oriented Questions Your Organisation Needs to Ask Itself

• Where do we experience bottlenecks in our supply chain?
• Are we getting the most from our systems and suppliers?
• Which system and suppliers are the most unreliable?
• What are the frequency and causes for service disruption?
• Do we have the right technology in place to maximise our data?
• Which are of our business do we see the biggest losses and why? (e.g., fraud, regulatory fines)
• Where do our employees spend a lot of time doing simple, repetitive tasks?
• How often do we repeat tasks and how many people are doing those tasks?
• Do we maintain a large business function to support seasonal peaks in demand?
• Are projects delivered on time and to budget? If not, why?
• What are the quality issues we face and how frequent are they occurring?
• What is the environmental impact of our business and how do we optimise it?
• Is our real-estate strategy optimally sized to support our workforce distribution?

By asking these questions, organisations will start to identify new opportunities to eliminate waste from their business. In turn, not only will improving analytical insights bring untapped value. However, machine learning capabilities can further support the reduction of people overheads, identify break-points, duplications of effort, and lag-time in their business processes. Whilst automation can be introduced with Robotic Process Automation (RPA) and higher value opportunities can be unlocked with self-service chat-bots. Indeed, bringing these two capabilities together, firms can further identify opportunities to simplify and streamline operational processes, which in turn can accelerate key business interactions with customers, partners, and intra-business teams.

3. Redefining Business Models and Exploiting The Monetisation of Data

Organisations are increasingly waking up to the plethora of data at their fingertips. However, the onset of public cloud, compounded with heritage technology stacks and increasingly stringent regulatory demands does not make this an easy win for all organisations. In respect of monetising data, three key opportunities can be pursued by organisations. They are:

• The data’s ability to increase the material value of the company.

• The firm’s ability to create value from the data, package it up and sell it as an asset to customers.

• The firm’s ability to package the data as a functional asset to third parties, suppliers and partners and use it as a bartering tool for reducing the costs of services and goods they provide to the business.

This is often where firms fall into a trap of collecting too much data. It’s a bit like keeping hold of your favourite Levis that haven’t fitted you for 10 years. They take up valuable space in your wardrobe and they are probably out of sync with fashion.

The same can be said for data. Namely, if you are obsessed with retaining and storing every data asset, notice that this takes time, costs more money and there is no guarantee that you’ll ever be able to unleash its potential. So it becomes stale, irrelevant and hard to manage. A bit like your off-vogue, bleached-white Levis!

Therefore, if your organisation is planning on trying to monetise its data it’s important to only collect the data you need, based on the services you want to provide and the target customers you want to reach. This is where the notion of treating data as a product can be super valuable,. To ensure your organisation’s data monetisation strategy is built on solid foundations, these are the types of questions you need answers to:

Monetisation Oriented Questions Your Organisation Needs to Ask Itself

• What data do we collect and how could this be valuable to customers?
• How large is the addressable market for this type of data?
• How would potential customers want to consume this data?
• What is the commercial model for our data product?
• Do any competitors exist and how established is their presence?
• Is there any legislation or regulations that we must comply with?
• What impact would this data product have on our business?
• Could it disrupt or erode existing revenue channels?
• Can we enter new markets or geographies with this data product?
• Can we use this data to barter with suppliers to reduce overheads and costs?
• Are there new partnerships that we can establish with this data product?
• How would we store this data and make it accessible to customers?
• What would be the cost to maintain and manage this data set?
• When can we make it available to potential customers?

To monetise their data, many organisations are establishing dedicated functions and business units to own this respective part of their corporate strategy. However, it is important to ensure that the data assets can be easily catalogued, discovered, governed and consumed in a traceable yet friction-free manner.

Final Thoughts

Throughout this blog, I’ve discussed the various opportunities organisations are gravitating towards to maximise their use of data. Whilst I have stressed that it is important for organisations to ask good questions, to establish the “Why?” that underpins their respective data strategy. Ultimately, to avoid drowning in a sea of data complexity, organisations need to develop a smart strategy that focuses on the data they need to achieve their strategic objectives.