What are the differences between Data Strategy and AI Strategy?

Without a strategy backed by data, you’re just another person with an opinion. Basing business decisions on gut feelings is rarely optimal – and it’s unlikely to be the best approach to spending your budget. But there is a better way. 

In this article, we look at two key areas that will help you make smarter, more cost-effective decisions: data strategy and AI strategy.  

These sound similar and indeed there are some commonalities – but also some important differences.  

Let’s start with what’s the same across both strategies. 

How data strategies and AI strategies are alike 

Whichever of these strategies you’re interested in, you’ll always need to start with your overarching business strategy. This means understanding from your stakeholders what the vision and direction of the business is so that you can align the data strategy and AI strategy you wish to formulate. 

Before developing either strategy, you should go through a discovery phase. This lets you perform a gap analysis, which measures where your business is versus where you wish it to be. From there, you can order the gaps by value. This defines the roadmap you need to plot a path to success. 

Discovery and analysis are essential to the success of our projects. We’re used to doing such work when our clients haven’t already taken these steps themselves. 

Differences between data strategy and AI strategy

That’s a brief look at similarities. But what about the specifics of each strategy?  

Let’s look next at data strategy. 

What is data strategy?

Keep in mind that data strategy and AI strategy fundamentally answer different questions. 

Data strategy is all about how you can improve your business using data, and is concerned with topics such as: 

  • How do we use data to make better strategic decisions? 
  • How do we use data to improve daily operations? 
  • What skills and culture are required in our business to maximise these opportunities? 
  • What governance is required to make this work consistently in the business? 
  • What data does my business have access to, and how can I rely on its quality? 
  • What technology do I need to support this?

 

A data strategy is required by any organisation that aims to be a “data-driven business”. While this is a common catchphrase, many people don’t know what it means in practice. To boil it down, being data driven means making decisions based on numbers rather than on gut feel. 

For this to work, the approach to being data driven must filter down culturally through the business, with all departments using KPIs for their business as usual processes. The organisation as a whole needs to develop enough “data literacy” to be able to do this, and that means that management have a responsibility to train and guide their staff. 

Data strategy also needs to consider the architecture of technology to suit the business’ needs. This means that, given the data in your business, you need to understand what is the appropriate tech required to make best use of that data so that you can make the right business decisions.  

This may involve the use of data lakes, data warehouses or other means of storing, processing and moving data around the business. (This happens to be one of our areas of expertise.) 

Data strategy is also concerned with answering questions such as the ones below. We include some industries here as examples of how getting the right data strategy can be of value:  

  • How can we get more revenue? Large retailers such as supermarkets are particularly focused on answering this question well.  
  • How can we be more operationally efficient?” Manufacturers are most concerned with making their processes cost less, with fewer errors and faster production cycles. 
  • How can we create smarter products and services?” Logistics businesses can add value to their clients by creating smart services, such as those that send text messages to clients at just the right times. 

 

Ultimately, all businesses that define a good data strategy are well placed to operate more profitably and hence strengthen their market position. 

What is AI strategy? 

AI strategy is all about how you can improve your business using AI technologies. You might assume that your AI strategy can exist only after a full data strategy has been defined? Actually, that’s not quite right. 

We often talk about good data being essential to good AI, and there’s no doubt that this is still true. But the reality is that no business is starting from zero when it comes to understanding their data – so AI strategy doesn’t need always require a robust data strategy first. 

That said, having a good data strategy in place means you’ll understand topics such as data governance and data cleanliness – and this definitely makes AI strategy easier. 

In AI strategy, we take the following approach: 

  • Review all the opportunities. 
  • Score and then rank those opportunities on their potential to create value for the business. 
  • Assess how much effort is involved for each opportunity. 
  • Measure how confident we are in being able to deliver each opportunity. 

 

Taken together, these considerations allow us to understand the risks for each opportunity. In an ideal world, we want to deliver a low risk, high value, easy and quick implementation so that our clients get the biggest bang for their buck. 

Defining a good AI strategy means being able to deliver a differentiated and profitable service to your clients. Consider a few examples: 

  • Clustering: this AI technique allows businesses to efficiently identify and target different customer groups. While traditional customer segmentation separates people based on demographics, AI clusters are based on behaviour – and this is far more effective when it comes to targeting people with the right products, services and messaging. 
  • Operational efficiency: AI systems can be trained to spot defects in manufacturing processes and to handle predictive maintenance to reduce failure rates and lower repair costs. See our article What are the applications of AI in manufacturing? for more about this.  
  • Smarter products and services: the possibilities can be seen via the huge array of existing AI-enabled consumer options such as Alexa, Siri, satnavs, automatic video captions, automatic language translation, auto-identification of faces in photos, and many more. 

 

This is the tip of the iceberg when it comes to what AI is and might yet be capable of. We do think that having a robust data strategy and following it up with an AI strategy is going to be essential for businesses to protect their market position and to grow throughout this next decade. 

Get your strategy right 

We believe that good strategies are important and we know from our clients’ experience that those who develop robust strategies are those who win in the long run. Put another way, before you dive in and spend big on any tech project, it’s best to spend a modest amount on getting your strategy right first. 

With our support, you’ll be best placed to become a more profitable, data-driven business rather than one than relies on instinct and opinions. Gut feel gets you only so far – the data always wins in the end! 

Need help getting your data strategy or your AI strategy right, and build a more profitable business?

Talk to us!

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