Business, Business

Early in 2020, many companies were already looking to innovate faster, today, the imperative for data-driven insights and digital execution has become first-order priority forcing businesses to accelerate business transformation efforts.

Enterprise Data Strategy is critical for unlocking the digital transformation, and 2020 is a wake-up year for many firms, as the total cost of getting data wrong will become clearly apparent, according to Forrester. Also according to Forrester, in 2015, about 58% of the decisions made by executives were based on intuition. With exponentially increasing amounts of data, unprecedented access to technology, and an increasingly competitive market, it’s imperative that organisations evolve past relying on intuition and start making data-driven decisions.

Today, organisations are continuously installing new applications to gain a competitive advantage and are establishing yet another source of data with each one. Our enterprise data truth is that digital information is now created by and accessible to the average non-technical user of applications and systems, without having to require the involvement of IT or by just bypassing IT. These “local” initiatives are an answer to the data fragmentation resulting from the multitudes of departmental apps, data in the cloud, and the diversity of enterprise systems yet creating an even bigger data fragmentation landscape.

A Gartner forecast indicated that this year more than 40% of data-based tasks will be automated to bring higher productivity and more democracy to the data user community. Taken together, these trends further escalate the need to solve the massive data fragmentation problem, and the subsequent demand for comprehensive data access.

Moving away from legacy processes and changing “but we’ve always done it this way” mindsets can be difficult, and many organisations are overwhelmed by silos of scattered data. By spending the majority of their time and resources on costly data migrations, organisations lose their ability to execute on any data-driven objective or decision. Furthermore, siloed data is a substantial roadblock to making large volumes of data actionable, comparable, reliable and timely.

The rationale for many digital transformation projects is often to increase top-line revenue by enabling more intelligent decisions, improving customer relationships, or empowering sales teams or to look to reduce operational overhead. Despite the business case appeal and the easy access to advanced technologies like data analytics, AI (artificial intelligence), or IoT (Internet of Things), some digital initiatives may not live up to their potential. A fundamental challenge is anchored in the current IT landscape realities: many different databases, often multiple systems of record, and multiple ERP solutions that facilitate the processes that enable businesses to run and operate. Furthermore, each technology solution has its own proprietary and complex data model; data is stored and often protected in silos, and the skills and expertise to make sense of the data within these systems are often held by just a few.

In these dynamic times, the cloud has proven to be an essential enabler in keeping people connected and businesses running on a global scale. More companies are speeding up their cloud transitions driven by the impact of the pandemic, which brings greater complexities in Data Management. Many large enterprises now run mission-critical applications across hybrid computing environments. Thus, beyond IT departments providing the connectivity between these solutions, there is a much larger challenge in translating the data held within these solutions into usable information for more than the data experts, but also for the business operators and decision-makers.

The data-driven enterprise is not just about our ability to capture and visualise the information from data sources. It’s about the transparency, comprehensibility, and relevance of the data models behind it. Without it, data fails to provide meaningful insights that empower action even more it forms the essential basis before even start thinking of implementing advanced technologies like AI and advanced analytics.

Steps for moving towards a Data-Driven Enterprise

1.    Data Fragmentation

With the democratization of data trend, business units not only create tremendous volumes of data, but also need to collect, store, and curate data to suit their own needs in disparate systems. A unified data environment addresses the inefficiencies and prevents errors from having multiple isolated versions of data. In short, connectivity among data sets and the ability to self-heal incorrect data in source systems are fundamental requirements for creating a clean version of the truth. Today, off-the-shelf data connectors for most common data sources give businesses a simple path to get started. Beyond the “flat” data, consider the application logic it lives in. Designing solutions beyond the connectors and visualisation layers is essential to enable data-driven decisions.
Clearly, the growth of advanced data technologies and the explosion of data sources makes the discipline of Data Management ever more complex. As we embark on a new decade, data connectivity is a strategic imperative for businesses to unleash their greatest asset – data. The ability to access and integrate data when and where it’s needed simply cannot be underestimated.

2.    Process automation

Businesses must be agile and “do more with less.” This requires an emphasis on finding ways to remove inefficiencies, thus the appeal of process automation — the use of technology like AI and Machine learning to automate repeatable, day-to-day tasks. An important change to be able to rely on automated and repeatable processes is having a solid data governance strategy in place. Data lineage and impact analysis are essential instruments in this context accompanied by a metadata dictionary. Creating a company-wide understanding and complete trust in your data — speed up business-critical decision making, increase your revenue, reduce your costs, and ensure your company’s data compliance.

3.    Self-service Analytics and capabilities

Self-service channels and platforms empower employees, customers, and partners to get what they need without relying on IT or business support. We’ve witnessed the importance of digital front-office operations, such as eCommerce and customer service apps to empower brands to stay connected and serve their customers. These digital channels generate tremendous volumes of customer interaction data, which requires adopting the right easy-to-use tools for businesses to gain faster access to data and collect insights about customers and their behaviours.

4.    Cultural change required for long-term success.

Encourage interest among employees to be more data-driven by providing them the opportunity to be creative and involved, virtualise your company’s data analytics and Data Science skill set, and always apply any changes gradually yet steadily.

In taking these steps, organisations can transform into data-driven enterprises and establish a solid foundation for building a modern Data Architecture that will scale along with future business growth.