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Leverage your data

Find out in the BARC infographic "Leverage your data" what current corporate practice looks like in the area of data usage.

A Data Strategy Checklist for the Journey to the Data-Driven Enterprise

Digitalization is on the agenda of almost every company, and data is the foundation of digitalization. Its availability and quality are crucial for digital success, making it an important economic asset for the business. It is therefore obvious that this asset should be carefully and farsightedly maintained and developed. But is it?

Data management is unfortunately considered to be a thankless task. Data experts know all too well that their company data is usually not in such good shape. They have been pointing this out for many years and often drive initiatives to address it, but with moderate success. The problem is that data is abstract and therefore difficult for non-experts to understand.

Business users often think that data is something technical that it is not their concern. They believe the IT department should take care of it. While IT is happy to look after the technical storage and backup of data, they refer to line of business experts when it comes to quality and usability. Managers see data as relevant in the context of digitalization, but often think of data-related problems as minor details that have little strategic importance. Thus, it is taken for granted that companies should have a data strategy.

But what is the scope of an effective strategy and who is affected by it? Why is it so difficult to create added value from data? Which business-related, technical and organizational challenges need to be resolved? What approaches are companies taking and what benefits have been achieved so far? Which conceptual, architectural and technological approaches can support these efforts?

Many declarations of intent regarding data, but serious investment lacking

In principle, everyone agrees that data is important, and its targeted use can make a decisive contribution to improved company results. But the fact is that data use is far too difficult today. However, investing in improvements is not usually a real priority. Decision-makers in particular have little insight into their data-related problems and the benefits of potential investment.

Best-in-class companies, however, are pioneers in this respect: they have already created transparency about the value of data and what can be drawn from it. They have thus created the basis for convincing decision-makers to invest.

A data catalog creates transparency, but requires buy-in from business users

First and foremost, leveraging data requires transparency: finding, understanding and utilizing the right data for individual needs. In this respect, simplicity is king – for data consumers from both business and IT. Providing an easily accessible description of individual data sources and their dependencies and processing flows is an important step.

Data catalog platforms are ideal for this purpose. The usability of data documentation for business users stands and falls with an overarching business glossary. In most cases, however, this will not be possible without the active contribution of business data experts.

Data democratization requires a new deal on how data is handled across the enterprise

Insufficient quality and availability of data are drivers for self-service analytics. However, this promotes a proliferation of varying data interpretations and has a negative impact on efficiency. Usability of data starts where the data is produced. Data producers need to understand and take into account which data-related needs data consumers have. At the same time, data consumers must understand the requirements and restrictions of data production processes.

Enterprises need a “NEW DEAL” between data producers and data consumers that effectively addresses the top three challenges to improving data handling – time spent, a lack of transparency of data value and insufficient data quality. The goal is to optimize company data in terms of a common vision in a cooperative and iterative way and thus to accelerate the digital transformation on the basis of data.

Architecture and technology play an important role in the transition to a data-driven enterprise

Architecture and technology for data and analytics is frequently associated solely with data warehouse and data lake environments. However, successfully leveraging data does not just begin with local data consumption. On the one hand, systems supporting smart processes must have a stronger focus on high-quality data generation.

In addition, their architecture must be made fit for digitalization. This implies both functional and data management requirements. The design approach must be holistic and aligned with the requirements of classic BI and data & analytics labs. End-user-friendly technologies must ensure that business users are actually empowered in terms of data democratization.

Enabling a data-driven enterprise requires a fundamental cultural change driven by the executive level

Technology is an enabler but not the driver for data-driven working. Individuals adapt to the corporate system. Corporate culture and organization must therefore be realigned. In this respect, the widely adopted bottom-up approaches to digital transformation are very limited in their impact.

Measures such as establishing clear responsibilities for data in the line of business, investing in data literacy by carrying out targeted staff development and training, and developing the corporate data culture from “need to know” to “right to know” require strategic orientation and active support by the executive level. You will also need a cross-functional team of mid-level directors and managers who have a vested interest in becoming a data-driven organization.

Download the full study here.

Infografik: Leverage your data

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Market Research Analyst

Annika conducts both product-based and topic-based BARC studies in the areas of BI & analytics, data management and corporate performance management.

As project manager, she is responsible for time and resource planning and oversees projects from questionnaire creation to publication.

Depending on the study, she also handles the implementation of project steps such as questionnaire design, field phase control, data preparation and analysis.

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