Converting Data Into Insights: The Right Technology Alone is No Guarantee of Success

The fact that technology is playing an increasingly significant role in executing many traditional finance tasks while at the same time generating greater insights that drive business performance is irrefutable.

However, even though many organizations are significantly investing in advanced data and analytics technologies, majority of these investments are reportedly yielding disappointing returns.

This is often because they focus mostly on the implementation of new tools, and less on the processes and people. For instance, there is a widespread misconception that data analytics is a technology issue, and also about having the most data.

As a result, many organizations are ultimately spending large amounts of money on new technologies capable of mining and managing large datasets.

There is relatively little focus on generating actionable insights out of the data, and building a data-driven culture, which is the people aspect of analysis.

Data analytics is not purely a technology issue. Instead, it is a strategic business imperative that entails leveraging new technologies to analyze the data at your disposal, gain greater insight about your business, and guide effective strategy execution.

Furthermore, having the right data is more important than having the most data. Your organization might have the most data, but if you’re not analyzing that data and asking better questions to help you make better decisions, it counts for nothing.

Reconsider your approach to people, processes and technology

The success of any new technology greatly depends on the skills of the people using it. Additionally, you need to convince people to use the new technology or system, otherwise it will end up being another worthless investment.

Given digital transformation is here to stay, for any technology transformation to be a success, it’s imperative to have the right balance of people, IT and digital skills.

It’s not an issue of technology versus humans. It’s about striking the right balance between technology and people, with each other doing what they do best. In other words, establishing the right combination of people and technology.

For example, advanced data analytics technologies are, by far, better than humans at analyzing complex data streams. They can easily identify trends and patterns in real-time.

But, generating useful insights from the data all depends on human ability to ask the right key performance questions, and identify specific questions that existing tools and techniques are currently not able to answer.

Rather than hastily acquiring new data and tools, start by reviewing current data analytics tools, systems and related applications. This helps identify existing capabilities including tangible opportunities for improvement.

It’s not about how much the business has invested or is willing to invest in data analytics capabilities; it’s about how the business is leveraging existing tools, data and insights it currently has to drive business growth, and where necessary, blending traditional BI tools with new emerging data analytics tools.

That’s why it’s critical to build a clear understanding of which new technologies will be most beneficial to your business.

Technology alone will not fix broken or outdated processes. Many businesses are spending significant amounts of money on new tools, only to find out later that it’s not the existing tool that is at fault but rather the process itself.

Take data management process as an example, mostly at larger, more complex organizations. Achieving a single view of the truth is repeatedly challenging. This is often because data is often confined in silos, inconsistently defined or locked behind access controls.

A centralized data governance model is the missing link. There are too many fragmented ERP systems. Data is spread across the business, and it’s difficult to identify all data projects and how they are systematized across the business.

In such cases, even if you acquire the right data analytics tools, the fact that you have a weak data governance model as well as a non-integrated systems infrastructure can act as a stumbling block to generating reliable and useful decision-support insights.

To fix the faltering data management process, you need to establish a data governance model that is flexible across the business and provides a centralized view of enterprise data – that is, the data the organization owns and how it is being managed and used across the business.

This is key to breaking down internal silos, achieving a single view of the truth, and building trust in your data to enable effective analytics and decision making.

Is your organization spending more time collecting and organizing data than analyzing it?

Have you put in place robust data governance and ownership processes required to achieve a single version of the truth?

Are you attracting and retaining the right data analytics talent and skills necessary to drive data exploration, experimentation and decision making?

More Data Doesn’t Always Lead to Better Decisions

Thanks to advancements in technology, our digital lives are producing expansive amounts of data on a daily basis.

In addition to this enormous amount of data that is produced each day, the diversity of data types and data sources, and the speed with which data is generated, analyzed and reprocessed has increasingly become unwieldy.

With more data continuously coming from social spheres, mobile devices, cameras, sensors and connected devices, purchase transactions and GPS signals to name a few, it does not look like the current data explosion will ebb soon.

Instead, investments in IoT and advanced analytics are expected to grow in the immediate future. Thinking back, investing in advanced data analytics to generate well-informed insights that effectively support decision making and drive business performance have been the paragon of big corporations.

And smaller businesses and organizations, as a result, have for some time embraced the flawed view that such investments are beyond their reach. It’s no surprise then that adoption has been at a snail’s pace.

Thanks to democratization of technology, new technologies are starting to get into the hands of smaller businesses and organizations. The solutions are now being packaged into simple, easy-to-deploy applications that most users without specialized training are able to operate.

Further, acquisition costs have significantly reduced thereby obviating the upfront cost barrier, that for years, has acted as a drag on many company IT investments.

While the application of data management and advanced analytics tools is now foundational and becoming ubiquitous, growing into a successful data-driven organization is about getting the right data to the right person at the right time to make the right decision.

Distorted claims such as data is the new oil have, unfortunately, prompted some companies to embark on unfruitful data hoarding sprees. It is true that oil is a valuable commodity with plentiful uses. But, it is also a scarce resource not widely available to everyone.

The true value of oil is unearthed after undergoing a refinement process. On the contrary, data is not scarce. It is widely available. Nonetheless, akin to oil, the true value of data is unlocked after we have processed and analyzed it to generate leading-edge insights.

It’s a waste of time and resources to just hoard data and not analyze it to get a better understanding of what has happened in the past and why it has happened. Such insights are crucial to predicting future business performance scenarios and exploiting opportunities.

More data doesn’t necessarily lead to better decisions. Better decisions emanate from having a profound ability to analyze useful data and make key observations that would have otherwise remained hidden.

Data is widely available, what is scarce is the ability to extract informed insights that support decision-making and propel the business forward.

To avoid data hoarding, it is necessary to first carry out a data profiling exercise as this will assist you establish if any of your existing data can be easily used for other purposes. It also helps ascertain whether existing records are up to date and also if your information sources are still fit-for -purpose.

At any given time, data quality trumps data quantity. That is why it is important to get your data in one place where it can easily be accessed for analysis and produce a single version of the truth.

Unlike in the past where data was kept in different systems that were unable to talk to each other making it difficult to consolidate and analyze data to facilitate faster decision making, the price of computing and storage has plummeted and now the systems are being linked.

As a result, companies can now use data-mining techniques to sort through large data sets to identify patterns and establish relationships to solve problems through data analysis. If the data is of poor quality, insights generated from the analysis will also be of poor quality.

Let’s take customer transactional data as an example. In order to reveal hidden correlations or insights from the data, it’s advisable to analyze the information flow in real-time; by the hour, by the day, by the week, by the month, over the past year and more. This lets you proactively respond to the ups and downs of dynamic business conditions.

Imagine what could happen if you waited months before you could analyze the transactional data? By the time you do so, your insights are a product of “dead data”. Technology is no longer an inhibitor, but culture and the lack of leadership mandate.

As data become more abundant, the main problem is no longer finding the information as such but giving business unit managers precise answers about business performance easily and quickly.

What matters most is data quality, what you do with the data you have collected and not how much you collect. Instead of making more hay, start looking for the needle in the haystack.