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Data culture trends and challenges in Europe…

The global economic backdrop is plagued with pandemics, energy crises, climate change, supply chain shortages, and other disruptive factors. Data is being touted as this era’s ‘new oil’, and the time is ripe for companies to harness this new oil to weather these disruptions and realise the potential it can deliver.

True, many data technology solutions are present in the market to advance this cause. However, having state-of-the-art technology won’t be enough if the people don’t appreciate the importance of data, take up ownership of data quality, and leverage data to drive any kind of breakthrough necessary.

Europe and South Africa have their own unique ways of rising to the occasion. More and more organisations want to establish a data culture that will drive the underlying data technology and IT systems – with the ultimate goal of propelling business growth.

But what is data culture?

It refers to the collective behaviours and mindset of how people view, treat, and use data to base their decisions upon for the organisation’s strategy. Decisions derived from data will help to improve processes, boost productivity, and further enhance competitive advantages. This has been a priority of industry leaders. A global IDC survey found that 83% of CEOs aspire for their organisations to be more data-driven.

There needs to be a bridge linking data culture and the supporting technology, as well as a comprehensive data strategy and guidelines that can be translated into practical applications to support and serve as the bridge. This is where data governance strategy becomes important to support and drive the structure, policies, rules, and access of data.

Involvement and input from various departments should be initiated to build the very foundation of this strategy. With ownership defined across the organisation, executing the governance strategy will be more impactful, encouraging people to use and trust data in daily tasks and decision-making.

As we have frequent interactions with companies from several sub-regions of Europe and South Africa, it’d be interesting to examine the trends, challenges, and levels of data culture adoption. More importantly, we’ll explore the action plans stemming from these data-driven mindsets and behaviours, as well as prescribe practical ways to overcome the data-related stumbling blocks.

Europe and South Africa: Data maturity trends

DACH countries

DACH countries, comprising Germany, Switzerland, and Austria, are a very mature group of industrial heartlands with advanced economies and huge capitals.

Since SAP was founded in Germany, the DACH region have a large SAP clientele base with most companies using SAP’s Enterprise Resource Planning (ERP) system since it was first introduced back in the 70s, and thus understand the value of structured data and the challenges associated with unstructured data.

Since most of the companies are mature in their respective industries, they have a solid grasp of their business and data processes, and had gone through several iterations of process optimisation using their current SAP or other ERP systems.

This explains their slow uptake in moving to S/4HANA, SAP’s next-gen in-memory platform that offers features like simplified business models, real-time transactional processing, and embedded analytics.

Due to Europe regulations and data protection policy, European companies are more comfortable running their operations on-premise, which most old ERPs run on, rather than in the cloud. There is also a significant cost associated with moving to the cloud—as it is a new implementation rather than a ‘simple upgrade’.

No doubt, a considerable number of companies have made the transition to S/4HANA. One observation worth noting is that data improvement and cleansing plans are largely absent. This is especially true for companies that have multiple SAP instances as a result of diverse operational areas and M&As (mergers and acquisitions). Due to the complexity even at the assessment level, activities like data profiling, deduplication, and enrichment hadn’t been included in their migration plans.

It’s also likely that they don’t know how and where to start, as they haven’t found suitable data cleansing tools and services to get them started. They just performed data migration without any prior treatment applied to the existing data sets.

Whether or not they move to S/4HANA, companies that have MDG (Master Data Governance) included in their overall SAP license hope to have a well-defined governance approach. Using this add-on tool, they have laid out the master data maintenance process, workflows, access, and data quality rules. This underscores their high level of awareness and maturity in data governance.

It’s evident that data culture is prevalent in most DACH companies (especially those that use SAP), but only in specific areas where a data governance framework has been put in place even before the move to S/4HANA. What they mostly lack is the awareness and application of data preparation, a crucial prerequisite in future-proofing their S/4HANA investments.

Benelux countries

The situation is almost similar in Benelux countries. Comprising Belgium, the Netherlands, and Luxembourg, they’re industry leaders in their own right. Compared to DACH countries, English is more widely spoken.

Most companies stay within the mid-market space. By comparison, the Netherlands have a well-defined presence of mid-market and enterprise organisations.

Those that implement SAP would already have established a data governance practice alongside their MDG license. Yet, it may not be that extensive, i.e., restricted to select master data areas only.

Similar to DACH, data improvement activities hadn’t been part of their S/4HANA migration plans. They only make use of the existing data governance framework to perform data administration beyond go-live. Essentially, they apply passive governance where data goes into the system first before undergoing validation and remediation.

As data culture isn’t fully embedded, there’s a need to shift the mindset from administrative to operational data governance. They have to expand beyond administering master data to inculcate an end-to-end data process that prioritises validation and ownership to preserve data quality. They should onboard more master data areas and business units into this framework.

This way, they can maximise the benefits of having a digital, in-memory platform of S/4HANA to beat mushrooming competition within their industry.

Nordic countries

As for the Nordic countries (Denmark, Norway, Sweden, Finland, and Iceland), the companies we’ve engaged are relatively small.

They have a low level of awareness of the value and importance of data; hence no data culture is established. Having said that, they’re willing to listen and educate themselves about the importance of data quality and governance as well as the associated technology solutions.

This can be a good thing for them as they’d have enough knowledge to plan their data culture journey and choose the most suitable data management platform once they’re ready for it.

South Africa

Against the backdrop of slow economic recovery, South Africa remains a promising avenue for growth and expansion.

While they lack the technological infrastructure and tools, they’re cognisant of the importance of data to fuel their operations and digital transformation. But there’s a lack of ownership to drive any kind of data governance plans that can manifest into practical applications.

The fact that they have low labour costs isn’t helping either. They can just hire a few more headcounts to perform data checking and remediation—cheaper than buying and deploying a data management platform.

While there’s awareness of the importance of data within the working levels, embedding data culture is largely absent. Top management needs to step up to champion this cause.

Common pain points

Although each sub-region within Europe and South Africa has a markedly different economic status and unique mindset that informs their behaviour and decisions around data, they have some common challenges. The challenges are prevalent in key industries like Oil & Gas, Mining, Pharmaceuticals, Chemicals, Energy & Utilities, Manufacturing, and Engineering & Construction.

Massive amount of data

Today, acquiring and amassing data is not a problem. The problem starts when companies have to manage and govern this wealth of data so it can be transformed into useable and trustworthy information that can be easily consumed across the organisation. For instance, one client we’ve worked with is looking at 3.5 million records to be profiled and cleansed.

As a rule of thumb, data should be strengthened with these 8 data quality pillars: Accuracy, Validity, Uniqueness, Completeness, Consistency, Timeliness, Integrity, and Conformity. These criteria should also be set up as rules to maintain high-quality data at all times, forming part of the data governance framework.

Master data should incorporate data quality standards – as it provides the business context to core data areas such as assets, materials, suppliers, and customers. Attaining the Golden Records for their master data should be high on companies’ agenda so they’re assured of a single, authoritative version of the truth.

In the absence of these pillars, people could end up using incorrect and outdated data to perform their tasks and make decisions, thus hurting the company’s outcomes. Instead of harnessing data to their advantage, they get buried under its avalanche!

They’ll need to have suitable tools to assess the health of their existing data, remediate errors, and enrich it with meaningful attributes. These steps are especially relevant when migrating to a new enterprise platform.

Uncontrolled supply chain costs

For asset-intensive companies, the challenges lie in managing and reducing free-text spend which can cause accumulation of duplicate spares and over-spending. This leads to more grave consequences like over-inflated working capital getting tied up in inventory.

The inability to control or track what’s in the inventory can increase the costs of storage, upkeep, and labour. While a huge working capital may look good on paper, it’d backfire in situations where companies need to free up cash for other projects or initiatives—or write-off aged stock.

This inefficiency can spread like cancer cells into procurement functions. Free-text data entries would bypass catalogues, so they’re not subject to any kind of data quality checking. They simply accumulate in the system. And it’s the ugly truth that people still consume the available data, regardless of the inaccuracies and incompleteness.

To highlight one devastating effect, Procurement personnel end up negotiating contracts on material groups that don’t even have substantial spending in reality, simply because they’re wrongly categorised through free-text POs. Missed opportunities to have strategic negotiations that cut costs!

Overcoming the challenges

It’s not a coincidence that data and process challenges seem to be intertwined. As data is the backbone of processes and operations, it’s only logical to address the issues through data.

Start by improving and cleansing data

Whether embarking on digital transformation through platform migration or getting their house in order through business process improvements, companies need to first come up with a strategy for data improvements. We recommend the following approach:

  • Data profiling
    • Technical profiling identifies and segments the technical attributes of data, e.g., format, length, and data type while business profiling refers to the business attributes, e.g., definition, usage, and context. This gives an understanding of the current data health for each master data area.
  • Business rules’ definition
    • Define business rules for each master data area. Ideally, it should encompass the 8 data quality categories to address current and future data anomalies holistically.
  • Data analysis
    • Compile data issues (e.g., duplicates, wrong format, missing attributes) and deep dive into measures to resolve them.
  • Data remediation
    • Fix the data issues using the agreed-upon business rules.

Asset-intensive organisations can utilise these steps to profile their assets and spares data, categorise them, and remove duplicates. They can go a step further by enriching these data sets with meaningful attributes. This provides the foundation for a taxonomy of materials’ classification and standardisation. It’d be easier for personnel to track them and choose the right ones whether they’re in inventory or to be purchased.

This is a sure-fire way to eliminate free-text spending and buying the wrong materials—all necessary measures to reduce working capital and drive down supply chain costs.

When applied to the bigger picture of digital transformation and overall process improvements, companies are assured of clean data from the get-go.

It’s also worth noting that technical/platform migration is a cost-intensive affair. Hence, it’s better to include data improvement initiatives at the start of the project. It’d be more difficult to launch these initiatives post go-live when funding has been exhausted.

Strategising beyond clean data

Now that they have gained a strong footing with clean data, companies need to devise a strategy for the treatment of new data that enters the system. Not incorporating this as part of data governance strategy can cause data quality to deteriorate. Facing repercussions from bad-quality data upon transitioning to a state-of-the-art platform can become a nightmare of corporate proportion!

Here, companies can apply active governance. Data that comes from multiple sources will be validated upfront using a set of rules before entering the primary system. It’s an automation of data collection and validation process, which explains why active governance is also known as “Data quality at the source”.

Companies that have existing SAP MDG licenses should assess and identify which master data areas they can govern and support. The gaps should be filled via new procedures or tools so the governance framework can encompass all key master data areas. This enables data governance to serve its purpose in improving efficiency and enhancing the productivity of resources and operations.

Recognise that automation is key

It’s ideal for companies to assign dedicated resources to govern data and champion its use. They’ll be a step closer to establishing a data culture where people have a sense of ownership and trust in their data to inform their business decisions.

But, the nitty-gritty details of analysing, validating, and correcting data may bog down the people in charge. As the volume of data grows, human errors become inevitable, thus compromising quality. They risk getting catapulted back to square one with poorly-managed, low-quality data!

Automation is the name of the game. It’s one of the key features that data management platforms offer, designed to make people’s lives easier.

Automation facilitates key data processes like master data maintenance from creation to disposal, data validations using business rules, and data harmonisation from different sources. With less human intervention, these processes are more streamlined, run faster, and aren’t prone to errors.

Organisations at the early stage of data culture can benefit from having automation in place. Data personnel can set these tasks on ‘auto-pilot’ so they can dedicate more time and effort towards encouraging usage and adoption of data amongst their peers.

The ROI from having a data management platform that comes with automation far outweigh manual resources not just in efficiency and productivity, but in providing a strong foundation for data maturity amongst employees.

Future outlook

While we’ve observed a varying degree of data culture adoption in Europe and South Africa’s companies, they have a mature realisation of the importance of data to power their business machinations. This is evident through the various levels of data governance implementation amongst the companies.

It’s also crucial for organisations to find the most suitable data technology solution in charting a comprehensive approach to data management and governance. Active and passive governance complement each other in ensuring continuous data quality for new and existing data.

With this governance framework available, and a positive attitude toward data as a solid starting point, the regions of Europe and South Africa are in a better position to withstand future challenges. Their data culture aspirations, along with a suitable data management platform can pave the way for them to become key industry players.