Only Data is Unique: Four Typical Challenges in the Transformation to Becoming a Data-driven Business
Artificial intelligence is a paradigm shift for all of us. The sooner companies realize this, the better they can tune their business for a new world order. Based on my insight into Danish and international companies, I summarize typical challenges in the transformation to becoming a data-driven company.
Artificial intelligence is much more than a new technology which can process huge amounts of texts, sounds, and images.
It's a General Purpose Technology (GPT) that will revolutionize our everyday lives in the same way that the industrial revolution did in the early 1900s.
Artificial intelligence is a paradigm shift for all of us. The sooner companies realize this, the better they can tune their business for a new world order.
In the future, companies that are traditionally management-driven must be data-driven. The companies that today work in silos - consciously or unconsciously - will in the future work interdisciplinary. Data is the raw material and the center of business development.
Only data is unique. Products and prices can be copied. It's the ability to leverage data in the most cost-effective way that gives companies the edge and opportunity to differentiate themselves. That premise is crucial to the future competitiveness of companies.
Fortunately, the premise is clear for most people, but it lags with the execution.
Based on my insight into Danish and international companies, I summarize typical challenges in the transformation to becoming a data-driven company.
Only data is unique. Products and prices can be copied. It is the ability to leverage data in the most cost-effective way that gives companies the edge and opportunity to differentiate themselves. That premise is crucial to the future competitiveness of companies.
Think Interdisciplinarity into the Strategy
Understand what data can do for your business and how artificial intelligence can become a strength for your business and you as an organization. Often independent activities are initiated. The finance department wants to automate routine tasks, marketing wants to collect experience data on the website, and IT is working to upgrade technologies, etc.
In this way, digital transformation has begun but efforts are uncoordinated.
When creating a data-driven business, it's necessary to prioritize and select the most important efforts and have less focus on where in the organization the initiatives arise, are implemented and benefit.
Digital transformation requires an interdisciplinary effort that must be rooted in the organization.
Employees' focus must shift from their own professional group to cross functional professional groups, and they must be motivated through joint initiatives and participation in those initiatives that are most important for the entire company.
To get everyone's buy-in, the efforts which the company selects must be clear and understandable. The responsibility for communicating this, relies within the top management.
Only Have One Version of Data
Create an overview and ownership of your company's data. Most companies experience conflicts of interest related to data across the organization. It goes from nobody wants to own and take responsibility for data - to the fact that several departments want to own and thus influence data.
When the same data is used in many contexts, data ownership is usually difficult to handle.
Who has the mandate to change or expand existing data in use?
Who has an overview of the features, reports, products, and services that build on this data and thus know about the potential consequences, it may have to change the data?
By ensuring that a data set only exists in one version with unambiguous ownership, you can reduce the complexity and instead achieve data consistency and data quality.
It requires an overview of what data you have, who owns the data, how it's used and where it is physically located. Systems supporting this becomes a necessity as the amount and use of data increases.
Understand Your Data and Data Results
It's necessary that you involve relevant professional skills in the project team in order to bridge data, IT and business. Thus, a project team must consist of both business specialists, a data scientist, and IT.
The business's professional skills are especially important to include in the project team when selecting data for analysis. On one hand, a to narrow data set can give erroneous conclusions, and on the other hand, an overly broad data set can cause the project process to be extremely long.
The business's professional is also important in understanding and interpreting data results. Because the results are often surprising.
Perhaps you were expecting to find data relationships, which does not exist? Or you find explanations based on data for something you never expected to be possible? In order to build confidence in the data results, they must be understandable and explained. It ensures the professional skills.
Begin by Transforming Product Development
Bring your attention on digital transformation in the perspective of developing new products and services. The reason is that by developing products and services based on data, your company can differentiate from competitors in the future.
The vast majority of companies are embarking on the digital transformation train, focusing on efficiency, automation, and dynamic reporting. The focus on automation is a valid investment. But with a risk of this happening at the expense of product development.
The competences working with product development are in many ways more challenging in terms of defining, prioritizing, and executing development projects where data is the focal point.
However, efforts are needed to ensure the company's existence.
Where digital transformation in automation ensures improved results in the short run, it's the ability to leverage data in new products and services that generate revenue in the long run.
ABOUT Rooftop analytics
Rooftop Analytics has many years of experience with both predictive analytics and project management. We understand what it takes for a data intelligence initatives to succeed, and not least the challenges that companies face on the way to becoming a data-driven company.
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