Building a Solid Data Strategy: A Practical Framework

Data. Sometimes it seems like there’s less strategy, more mess. And that means more stress. But that doesn’t always have to be the case. 

The data strategies you put in place right now will help your company deal with the multitude of data challenges they face. It’ll even shape how future issues and disruptions are tackled. But there’s challenges at every step. 

But don’t panic, we’re here to help. Put down the stapler, refrain from sending that angry email, and step away from the fourth cup of coffee. We’ve got you. 

We understand the panic, too. 2.5 quintillion bytes of data is generated daily. 90% of data has been generated in the last 2 years alone. So, what are we going to do with it?

Before we jump into the how, we’ve got to figure out the what, and why. So, let’s start with:

What is a data strategy?

Managing your hoards of data is critical to businesses success. But even with the emergence of Chief Data Officers, many organisations are still way behind the curve. 

According to cross-industry research, the majority of structured data within an organisation is not effectively utilised in decision-making, with less than half being actively employed. 

Additionally, a minuscule percentage, less than 1% of unstructured data undergoes analysis or utilisation. 

But with a dedicated data strategy, this can be avoided. Phew.

So, first things first. You’ve got to look at your business through a data lens. 

Sounds simple, right? Well, it can be. It’s all about having a perspective of the data you’ve collected, how it’s going to be utilised, and applying it to every decision you make. But remember, this is not a short term plan.

Three important aspects to ensure the longevity and success of your data strategy are as followed: people, processes, and technology. This means accountability, efficiency, and resources. 

It’s also important that you have a specific use case, an issue you want to tackle. Without this, developing a strategy is not going to happen. 

So, start with 4-5 use cases you already have, figure out how to get the solutions to those use cases, and start drafting your data strategy around these use cases. 

Now we’ve done the what, it’s time to look at the why.

Why do you need a data strategy?

TLDR; You need a data strategy right now due to the acceleration of data collection.

But let’s take a deeper look. 

Businesses globally  are confronting the most formidable challenges of our time. Geopolitical uncertainties are exerting pressure on supply costs, while the lingering impact of COVID-19, coupled with a global scarcity of digital skills, continues to be felt.

Simultaneously, the accelerated digital transformation, fueled by the pandemic, has generated an unprecedented abundance of data and unleashed immense potential in the field of AI. 

Businesses worldwide are investing billions to harness this potential, placing data at the core of new business models and technologies. 

So, that’s a hell of a lot of data to play with. But without structure, it can be a liability. 

By implementing a robust data strategy, businesses can enhance their decision-making processes, stimulate innovation, safeguard sensitive data, and unlock the complete potential of their data assets. 

Through these investments, businesses can experience improvements in financial performance, heightened productivity, and a competitive edge over their rivals. 

Sounds pretty good, right? If that’s convincing enough, we’ll jump onto the next section:

Introducing: the 6 pillars of a solid data strategy 

Now it’s time for a step-by-step guide into how to set up a top tier data strategy. 

Identify

Let’s start with identification. This is all about finding where your data is stored, and categorising it. Currently, businesses tend to have many departments collecting data from three different sources. We’ve even included a handy table below so you can get a closer look at where your data could be. 

Local Storage	Cloud Storage	Application
Databases	Drives	CRM
Files	Databases	Social Media
Scans	FTP	ERP

Question whether you are using any databases, are there any applications that store files, and also check if there are any scanned items you are storing on a regular basis. It’s important to start discussing with your team where they store their data. 

Then, when you’ve done this, it’s on to the next step.

Map

Next, it’s on to mapping your data sets.

Each department talks to each other, in one way or another. 

There’s always a relationship between different applications and different departments, such as marketing and sales, and sales and manufacturing. 

So, you need to map out how each application is linked to the other application. The trick is combining this data and creating a compelling story by joining everything together in one single place. 

Data Governance

After this, it’s all about policies and procedures. 

There has to be some integration to the data that is collected, after all you are collecting data throughout the entire lifecycle. 

You have to ask: is the data correct? Is it clean? There’s no point in collecting huge amounts of incorrect and unusable data. 

If your data is in the second category, you have to ask which business rules can be put in place to easily identify incorrect data and make quick corrections. 

Governance also includes security. Data includes personal and confidential information. You have to make sure the data is accessible by the correct person, and at the right time.

Over 70% of employees possess access to data that they shouldn’t have, while a significant 80% of analysts’ time is consumed by the arduous tasks of data discovery and preparation. 

Governance is the way to avoid this.

Classify 

Next is functional classification and data segregation. You have to ask if your data is:

• Internal or external
• Structured or unstructured 
• Quantitative or qualitative
• Static or dynamic
• Application based or use-case based

Then, you can move onto the next step.

Insights 

All of this is meaningless without insights. This is all about making sure all department Heads can use the data they collected. 

There are many different tools you can use to do this, such as reporting tools and data analytics tools, for example, which can help you generate meaning from the mess. 

This tends to start with the business intelligence implementation plan. Check out an example below.

Culture 

So, we’ve done all the technical stuff. Now, it’s all about making sure your data strategy is sustainable.

Data strategy can’t be seen as an on-off thing. It has to be embedded into your organisation’s culture – it has to be data-centric and data-driven. 

Start with the benefits for the individuals, what they would be able to achieve by being data-driven. Everything has to be incentivised, so these actions are fulfilled. 

Collaboration is vital, too. All your teams should be working together, not in silos. This can be hugely damaging to your strategy. More on this later.

The impact of having a data strategy 

According to the IDC, global investments in AI have witnessed a remarkable growth rate of 20.7% – and this number is growing. This means more data, more problems. But also more potential.

Despite this progress, a significant 56% of organisations continue to grapple with low levels of data quality, as reported by Experian in 2022.

Without strategy, this data is not utilised to its fullest ability.

As per findings from a report by McKinsey & Company, organisations that adeptly leverage data and analytics possess the potential to enhance their profitability by a staggering 60%.

Recognising the strategic value of data for their enterprises, numerous large organisations across the globe have appointed Chief Data Officers (CDOs) to oversee and manage data as a valuable asset.

An effective data strategy can impact your business in the following ways:

• Improved decision making 
• Increased efficiency 
• Increased revenue 
• Better customer service

Common challenges to overcome 

But it’s not just smooth sailing. Data strategy implementation doesn’t come easy. But the way to tackle these issues is to identify them first. Let’s jump in.  

Data silos – working individually or working with outdated data is a no-go. This can be done by lack of feedback from various teams, or the inability to translate this feedback. So collaboration is key. 

Lack of resources – Setting up a data strategy is a long time, full time job. So, there has to be accountability. There has to be a specific role within the business, planned resources, and planned personnel.

Lack of budgeting – Most aspects of businesses have a budget, from marketing to events, to tech. But not for data strategy. But data strategy takes time and resources. So a budget needs to be allocated for this. 

No commitment – this isn’t a one-and-done situation. Data strategy is, as we’ve said, long term. It is also constantly evolving. So, you have to keep modifying, changing, and working on your strategy to keep it working to its fullest potential. So, resources and budget are vital to show commitment. 

Cracking the Data Code

Now, you can approach data in a stress-free manner. It is essential for every business to purposefully prioritise their data and establish a well-defined strategy to effectively manage and utilise it for the advantage of the organisation and its customers. 

If you think your data strategy is a bit wanky or if you’re starting from scratch, let’s chat.