Data Strategy: Implementation, Challenges

It is undisputed that a data strategy is of great value to companies. In Customer Service, employees can see the relevant customer communication at a glance.

Decision-makers can minimize the risk of bad investments the more variables they take into account in their models and the more data they take into account.

Real-time data can be used to manage e-scooter or car-sharing fleets effectively, and traffic can be controlled intelligently.

One example could be given after the next, showing how data has already made our everyday working life easier, how companies are optimizing their processes and how data is fundamentally turning out to be the heart of innovation.

But to use data efficiently and intelligently, companies need a big data strategy. Otherwise, there is a risk of data chaos instead of data euphoria. In recent years, companies have put much effort into guaranteeing that ever-growing mountains of data can be structured and used intelligently.

The cloud is the basis for an intelligent data ecosystem.

By building a cloud infrastructure, companies ensure multiple users can access the same datasets from anywhere. Data flourishes between individual solutions via interfaces.

The cloud is the backbone of an intelligent data ecosystem and data strategy. And because of the storage of personal and sensitive data, companies are investing in compliance and security solutions – an increasingly important topic, especially given increasing threats and cyber-attacks.

Ransomware attacks have skyrocketed. For the data to be used by organizations, it must be incompatible formats. Software is required to enter or process data.

The success of a data strategy depends on automation.

How effectively decision-makers and employees use data as part of a data strategy depends largely on the degree of data automation. Which and how much data can easily be taken into account? And how relevant are these for the respective issue?

The limits of human capacity often limit business intelligence: for example, when customer service employees have to filter out ad hoc during a telephone call which information from a long customer history is relevant to the current request.

Another example: Even if a company’s decision-makers view reports to optimize investments, the validity of such a report stands and falls within the limits of human capacity.

After all, most reports in companies are still created by human employees. To do this, they ask departments to create models with different variables, enter data and create dashboards. In progressive organizations, this process is largely automated. But these models still need to be revised because people compose variables.

Implementation of artificial intelligence

This is exactly where AI solutions come into play. Because the more companies have automated their data strategy, the easier it is for them to take the next step and implement artificial intelligence. This is also shown by the process that applies regardless of an existing data strategy:

• Prioritization and review of possible use cases: The aim is to identify in a matrix where high added value can be created with little effort – and what the timeline looks like to where artificial intelligence de facto helps employees work more productively and make decisions optimally.
• After identifying the use cases, the phase of detailed scoping begins: Typical questions to be answered in this phase are: What exactly does the problem to be solved look like? Who should use the solution, and in what way? What are the key figures that can later be used to assess success? After this phase, a project plan should exist, including a document outlining a solution.
• Working with the data begins: These are prepared, and the first models are created and tested. Important questions are: Are the formats all compatible? Do the interfaces work?
• The data product is created as soon as everything works, and artificial intelligence starts its work. What those responsible too often neglect, however, is that artificial intelligence stands and falls with the fact that people benefit from it. These people must be trained on how to use the respective solution in everyday work. Ideally, this also applies after a project has gone live.

The challenge lies more in the data strategy than in the technology.

The bottom line? If organizations have already implemented a big data solution, defined data formats and have functioning interfaces, the effort involved in working with the data is significantly reduced. In this case, it is more strategic and operational issues than technological hurdles that pose real challenges.

Suppose organizations already have an efficient and modern Elastic Compute Stack such as Snowflake or Spark for Kubernetes and qualified employees for maintenance.

In that case, they have already done much preparatory work for artificial intelligence. Companies must then guarantee clear processes and a clear governance structure in advance, especially on the machine learning operations side, and ensure that thanks to appropriate functions for cataloguing data, everyone can see which data is available for which use case.

But above all, and this is often neglected, decision-makers must ask themselves the following question: Which use cases do we want to focus on with AI? We should remember: Theoretically, AI could help a company in hundreds of different use cases.