The Analytical Enterprise: Turning Data into Future Insights

The Analytical Enterprise: Turning Data into Future Insights

Every business wants to see into the future. With predictive data analytics, they might just be able to.

Before they can run, however, businesses need to learn how to walk. Simply put, while decision makers want to confidently predict the next decade or so, most enterprises would settle for an accurate picture of the present.

Data analytics is not just an appealing prospect, it is becoming a digital imperative. And it’s easy to see why.

There is not a single sector that does not stand to benefit from better understanding its customers. Similarly, every business on the planet would operate better with access to accurate predictions on the future. As societies – and businesses – get increasingly connected, a significant gap is forming between those who are successfully leveraging the data in those digitized connections.

In fact, we are already seeing the impact of improved analytics in many sectors.

In healthcare, predictive analytics leads to earlier disease detection and better management of chronic conditions. In retail, predictive analytics results in a more satisfying customer experience journey, better customer service and improved operations. Industries – like fintech – are using data and predictive analytics to tear up the old rule books and fundamentally disrupt their sectors.

However, a recent marketing survey conducted by Gartner said that just 14 percent of companies were able to gain what they called a holistic, 360-degree view of their customers through solutions such as a Customer Data Platform (CDP). By leveraging this information, those entities were able to introduce insight-driven product improvements. On the flip side, 82 percent of respondents said they hoped to be able to achieve a complete view of their customers in the not-so-distant future, with 60 percent saying that they believed they needed every possible data point for success.

Other industry studies confirm these findings and although businesses are trying hard to improve their use of data, many are still struggling.

Data is the Solution and the Problem

The role of data has shifted dramatically in the last 15 or so years from something enterprises were required to store and tended to think of as a cost center, to the biggest value driver of the digital economy,

Data is a commodity, a dynamic resource that holds the key to unlocking current market behaviors and, even more tantalizingly, future events. There is a consensus among business leaders that a solid data foundation is the first step on the path to realizing the benefits of cutting-edge AI tools –  Gartner recently found that one third of businesses are investing heavily in data reliant initiatives, for example.

Frequently the biggest impediment to gaining data insights is the data itself.

Data is powerful, but only once it is in a usable state. For most enterprises, the different states, locations and architectures in which the data resides first need attention through a process that’s known as data transformation – we covered this topic in more detail in  a recent Apexon blog.

The sheer complexity of data and the fact that it is always growing, combined with limited skills, time and resources within enterprises have all meant that despite robust ETL tools existing, the job of data transformation is for many an ongoing project that never quite gets finished.

Cost can be a major inhibitor, especially in cases whether the scope is large or very complex. When it comes to data transformation projects, highly focused strategy and leadership is particularly important to ensure projects stay on track and in budget.

Firstly, there must be agreement on what data needs to be collected and transformed. Being selective is key, otherwise enterprises find themselves biting off more than they can chew with data transformation initiatives that are lengthy and slow to deliver ROI.

Secondly, keeping focused is another important discipline since data projects tend to get derailed, either because another more urgent priority crops up or because well-meaning stakeholders keep adding to the original scope of work.

The question that needs to be asked, therefore, is how do we not only solve the challenges of understanding the data but also ensure that the actionable insights are the platform for effective decision making.

Data-driven Decision Making

Data integration challenges help to explain one of the major reasons behind the growing adoption of CDPs, a new breed of data platforms that provides a unified view of customer data, effectively breaking down the siloes currently hindering enterprises. These platforms effectively lay the foundations for advanced analytics and AI-powered tools. You can read more about CDPs here.

However, while processes like ETL and platforms like CDPs form the essential building blocks of data transformation, tools alone will not deliver data-driven decision making within enterprises.

Increasingly, businesses are looking inwards at how they organize themselves – Gartner calls this “decision intelligence” and defines this major trend in data and analytics as “understanding explicitly how people, machines/technology and data come together to inform and make decisions.” The analyst concludes that decision intelligence “is fast becoming a source of differentiation and survival.”

It is increasingly evident that there needs to be a level of data competency among business leaders and other stakeholders for insights to be turned into action. Technology plays a growing role in delivering these insights – great visualizations are a make-or-break component, not just something that is nice-to-have – but accountability must also rest with a workforce that understands the data, the strategic vision driving it and that is empowered to make the necessary changes based on the data’s findings.

Developing Data Literacy in the Workplace

As the role of data in decision-making become more prominent, employees will be required to become more data fluent.

Industry studies repeatedly point to the importance of data skills in the workforce and most business leaders reportedly see it as vital to company success. The reality is that in many areas of the business, data literacy has been seen as an added bonus rather than a skill that is nurtured, trained or hired.

Moving forward, enterprises should consider how data competency can be codified into the organization. There are many ways to achieve this skillset, but one approach would be to start by fostering an attitude of curiosity, with employees being actively encouraged to question data sources, how and why data is gathered. Another way to embed data literacy would be to ensure tailored data skills training and to incentivize data fluency formally in, for example, role descriptions and 360 reviews.

Data Insights Start with Data Strategy

The consequences of data illiteracy are more profound than the underperformance of any one particular business.

As society increasingly relies on data analytics and AI to perform everyday functions like loan applications, insurance claims, recruitment and many more besides. The ability to critically evaluate models will result in decisions that are fairer, more responsible and more accurate.

By contrast, leaving data modelling to just a handful of people risks unwittingly perpetuating biases and inaccuracies. Eliminating discrimination in data modelling can only be achieved if more, diverse people take part all along the process. Data strategy, which is often viewed by those outside the IT department as a kind of data tools roadmap that magically spits out business decisions, actually needs to be understood more widely among business leaders and map directly onto business strategy.

Data buy-in from the business is increasingly becoming a differentiator between the data-savvy enterprises and those who are struggling to implement analytics skills. A recent article in Campaign that focused on predictive analytics in marketing said that 61 percent of marketers plan to implement predictive analytics in 2022, but 38 percent of those respondents were still manually integrating data.

What is particularly revealing is the apparent correlation between low data maturity (for example, manual data integration) and low trust in data accuracy. On a very basic level, if people don’t believe the data, they will not use it to drive decisions. By contrast, the marketers already using predictive analytics already were more likely to have stronger strategic skills, for example, stronger reporting capabilities or better campaign optimization.

Deriving Future Insights from Today’s Data Practices

As widespread digitization takes hold, the opportunities can be transformational. We are all aware that we live in an increasingly connected world, but being able to literally connect the data point dots is critical.

There is no hyperbole in stating that the ability to transform data into insights is an “evolve or die” strategic imperative for businesses – this excellent article in Computer Weekly outlines the challenges – and importance – of data integration, and warns of the dangers in failing to rise to the data challenge, with the news source noting that “Data risks losing its freshness and relevance. Instead of a working tool for the business, conventional approaches risk creating a ‘data museum’ – useful, perhaps, for looking at past performance, but not for real-time or predictive analytics.”

Indeed, dynamically engaging with a data platform will help businesses generate data insights they can trust, leading to improved understanding of the state of the business and its customers and will help direct future business decisions.

Being able to maximize and monetize the value of the data available is already a standard requirement for an effective business optimization strategy and, importantly, a crucial indicator of digital maturity. Understanding and interpreting available information has always been what sets some brands apart from others, what matters now is that transforming business insights through data and predictive analytics should be at the top of list for any company that wants to remain relevant in our digital society.

Apexon’s teams of digital engineers have been able to solve many of the toughest challenges that come with digital transformation. We have a proven track record of integrating end-to-end digital lifecycle solutions, with a focus on ensuring that these digital strategies are done right first time and every time.

To find out more about Apexon’s Data Engineering Services click here. Alternatively, contact us today by filling out the form below.  

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