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9 Signs You Need a Data Strategy

What Is a Data Strategy?

The term “data strategy” refers to all your company’s initiatives and activities that revolve around data and technology to drive your business forward. Be it the databases you are using, some performant algorithms, or integrated services that fuel your automated pipelines. Here, we’re talking about all your efforts geared toward achieving a single goal: to improve business and generate revenue for your company with the use of data and its adjunct technology.

In our eyes, every future-oriented company should have their direction set and the initiatives in place so that they can improve their business with and through data. Yet, it’s easier said than done. Deficiencies in the strategic use of data are all around. In this article, we discuss 9 signs that can tell that your company should revisit the current or implement a new data strategy. We give you our guidance on how you can spot these signs and our recommendation on how to resolve them.

How You Can Spot the Signs

9 signs you need a data strategy_SiegfriedEckstedt

#1: The Lack of Smart Business Decision Making

“I don’t know. Let’s just ask the boss.”

When the business decision making is dependent on the bosses. Having a lack of smart business decision making usually shows in acting upon the HIPPO principle. That means that you would rather ask the ‘highest paid person’s opinion’ other than acting upon knowledge that you can obtain yourself – from data, or through analysis.At this point, we recommend that you revisit the questions in your business decision making. Revisit your general strategic business goals and define the questions that you want to answer  — on the back of data and through analytics  — in order to reach your business goals.


#2: The Lack of Advanced Data Skills

“Let’s better use Excel for that.”

At the time Excel is the one-size-fits-all solution to all your data problems, you might consider levelling up on your data skills. Excel is a wonderful tool but getting stuck on it reveals that you’re not moving into reliable data pipelines, advanced analytics, or data-enabled smart applications.Clearly, you need to move on now. Equip yourself with the knowledge you need to do the exciting things. You can trust the following approaches to effectively propel data skills in your organisation: enroll with data academy, set up a data garage, introduce data mentoring. Or think of more creative ways, like data internships, book-a-data-guy services, et cetera.


#3: The Lack of Customer Insight

“I believe our customers would really love that, no?“

You have a clear sign that you don’t know your customer, when strategic decisions and tactical choices are mostly based on assumptions, feelings, or some hopes and wishes. Closely related to this phenomenon, is that you often find that either Marketing or Sales cannot really answer why one campaign worked better than the other. And of course, any prediction of how the next campaign might go, would end up in even more confusion.Therefore, we suggest that you review the open questions that your Marketing, Sales, Product or Service team has not answered yet. Review what data you need to answer these questions. Summarise the findings in an overview of data you own and don’t use, and data you don’t possess but wish to use.


#4: The Lack of Smart Products

“But where is our great AI product?”

At the moment you realize your competitor launches smart products and you don’t. At the same time, you are still wondering about how to make sense of this whole AI buzz for the products that you sell. It is easy to conclude that your products do not use the data from the interaction with your customers so that you can improve the material, design, experience, servicing, et cetera.When this happens, you can start understanding the virtuous cycle of data products. Explore ways to bake the collection and use of data into interaction of your products with your customers and propose a design on how to provide actual value to your customers through the application of AI. As a final step, you can test your prototypes on your lead users.


#5: The Lack of Smart Processes

“Gosh. Let’s just get some interns in for that job!”

This is when you see that your single source of automation is pushing the task down the hierarchy. Why does this happen? Because things can get messy, and you get to have it done anyways. Pushing the ‘Let’s get the juniors do that’ button seems to be compelling because you can rely on juniors to do redundant work. However, this is a clear sign that you are not using your team members for creative work.

A good process will lead to good results. Plotting out the key platforms that you are using across the team, departments, and the entire organisation will help you with this issue. On the other hand, start analysing what processes are most important or valuable to your business and make a strategic decision on what process to improve. On top of everything, focus on the interfaces of user and system so that you can collect data and improve the process over time.

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#6: A Lack of Valuable Data Assets

“That’s actually super-hot data, no?”

“Data is the new oil.” You heard that claim and you don’t want to miss out, so you go out on a data hunt. And Hurray! You found a data treasure – a truly endless source of the best data revenue. But when you present your case to your data customers, there is no initial interest. “They don’t understand,” you convince yourself. You continue selling hard. Still, there is no interest. And you question, “what happened? Something wrong with you? Are all your tentative data customers nuts?” No, it’s a very normal case of a lack of valuable data assets.

If you faced a situation like this, we suggest that you change your approach in digging for data oil. Do not start with the data. Start with the users – your data customers – to get the feedback on what data they need to feed their decision making, smart products, or processes. As you are firm about what question your data can answer, target it to the customers that have the largest interest and deepest pockets of course.


#7: The Lack of Integrated Data Platforms

“It’s a spaghetti system.”

We can recognise spaghetti systems in the duplication of work, where different tools are being used to access and store data across different business domains. This creates an isolated siloed system in each team, department, or business unit. As a worst-case scenario, you may imagine that two teams talk about the same data, but the numbers don’t match across the teams’ systems.

To unpack your spaghetti system, start to understand your current system and particularly how it is used. Focus on the business routines and processes that are involved. Document every part of your analysis as you back engineer the spaghetti system. Emphasize on working with visuals, so that non-technical contributors can understand the dimensions of chaos you’re facing. When designing any new system, include power users of the current systems that can teach you the lessons learnt.


#8: The Lack of Focus on Strategic Data Use Cases

“Welcome to our project graveyard.”

When you see that the majority of all data science projects make it to the prototype stage but not reach the production stage, we need to review the data strategy. This is what we call the graveyard of data projects. With a lack of focus on the most strategic data use cases, companies put project by project to the graves. Over time, rather than making a targeted strategic effort, they have created a whole range of morbid, compartmentalised projects that never made it to a product.

We recommend overcoming this by starting to think about the business value as early as possible. For instance, discuss how each use case brings value to your customers and improves profitability. Do not execute any use case that may sound exciting to your engineers, or product managers. Bring in a multidisciplinary audience to discuss the potential business impact and select not more than three majorly transformative use cases. Then, quite naturally, when kicking off a new project, you will do it with the eventual product, the customer value, and business profit in mind.


#9: The Lack of Return on Investment

“We’ve only burnt money on all this new stuff.”

Potentially your company has started to implement a data strategy. That is a good start. But, many don’t consider the value extraction and the potential profitability that lies within each case. For those who claim that they have so far only burnt money with their data endeavors, we see a sign that the current business design doesn’t allow them to profit from data capabilities.

With this situation, we recommend that you intensely include the C-level to decide on what business goals to push and what value to drive. Create a link between the C-level, your data and technologies, and the business results. You can choose a small set of lighthouse projects first. Focus on the most valuable use cases. When ranking the questions that relate to each use case, consider the impact on business results with each question. And generally, please explore use cases that create value, other than just cut down costs and improve efficiencies.


Wrap up

In our view, companies must implement data strategies to leverage their data as an asset as the world becomes more technologically advanced. Data is used more efficiently when a dedicated strategy is in place.

Through the development of effective procedures and practices for managing shared information across the business, you improve meeting organizational objectives. Organizations that have not yet implemented a comprehensive data strategy and management system should do so as soon as possible. Keeping up with the changing business world ensures that you remain relevant.

We assist firms in this endeavor by redefining their data strategy. Reach out to us if you would like to know more about our data strategy workshop offering.

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