Clemens Baader, Director, International Personal Finance plc

There has never been a better time to access information. Data and analysis capabilities have developed rapidly in recent years. More advanced algorithms have been developed, computing power and storage have steadily improved, while their use is much cheaper and the amount of data available has exploded.

The convergence of these trends is fueling the rapid development of data analytics and artificial intelligence. Data and analytics are also changing the nature of competition. Leading companies are using their data and analysis capabilities not only to improve their core operations but also to launch entirely new business models. Data is now a critical asset for a company.

An ongoing challenge

While talking about data as the most valuable resource in the world is widespread today, several technological and business difficulties make understanding the meaning of data and understanding the opportunities an ongoing challenge.

Organizations that are relatively new to data and analytics have exceeded expectations for their potential value and return on investment (ROI). These inflated expectations can often lead to misdirected use cases,

misuse of capital and increased chance of failure. Plus, salespeople and consultants are just too happy to try to make money from new trends and passwords.

Data and analytics implementations typically focus on data storage, management, and administration — with an emphasis on project-based and operational measures such as scheduled delivery, budget, and scope. However, the creation of value from this data in terms of measurable benefits and return on investment is ignored. All too often, the “build first, think value later” approach is depressingly common. Wrong architectural and infrastructure choices often lead to projects that do not produce business results to achieve the promised and expected return on investment.

An effective approach

So how can we approach this more effectively? There are some key principles that we have adopted in the IPF to ensure that the return on investment is real.

In addition to all the “sophisticated noise,” all commercial businesses have exactly two basic needs: to increase revenue and to lower costs. Therefore, everything we do with data must ultimately strive to achieve these goals.

Within data and analysis, we should only focus our resources (i.e., people, investment, opportunity costs, etc.) on initiatives that support either of these two needs and where the estimated impact significantly exceeds the expected costs.

This underlines the importance of setting clear goals for your data and setting up analytical initiatives before you begin. Chasing hype simply by transferring data from one platform to another is unlikely to yield any concrete value. Simply pouring data into a new data warehouse and “wishing for the best” is not a strategy.

To ensure that we don’t just hope for the best, our IPF team clearly brings together the opportunity so that we can prioritize pilot opportunities that allow us to clearly develop our capabilities and ensure that we design and implement policies.

We pack all these elements from their packaging.

1.First, it is important to evaluate the expected value of each of the proposed data and analytical capabilities. This may be more than an “estimate” of an accurate estimate, but it is important to think about what valuable input each initiative is expected to make in advance.

1. Prioritize the most promising analytical options and use cases based on the expected value of each option and prioritize an opportunity roadmap (what to do, when) based on expected ROI and feasibility.

1. Next, it is important to test use cases to develop data and analysis. In IPF, we first experiment with cases to create a proof of the concept (‘trial and error’ to avoid declining economic J-curves). We will then ensure that we continue with other uses if and when the expected value of the data is determined.

1.After all, the importance of designing and implementing a target business model is key. For example, organization, people, processes and administration, architecture and capabilities. We then build a policy around this and implement the planned operating model.

So while it is true that data is now a critical business resource, without careful discipline it can only be hyperbolia. Companies that use data and analytics effectively to improve their business and are genuinely looking for new ways to earn revenue have the power to unleash a huge impact and get ahead of the competitor. Those who pay for the latest lips in fashion are in danger of losing the opportunity to change the company for the better.

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