Is your Data Management Strategy ready for ‘Digital’?
James Blackburn
By James Blackburn
Blog

Data Strategy Blog.pngAt AgilityWorks we believe that companies need to adapt their business strategy to take account of the opportunities of ‘digital’. For organisations there are clear opportunities such as: to develop a leaner enterprise, to reduce ‘time-to-value’, to better connect with customers and to innovate products and services.

Data is at the heart of such transformation as projects typically involve the implementation or integration of new business systems. The success or failure of digital projects is directly influenced by organisations’ ability to manage their data. 

Leading organisations already take data management seriously

It is clear that leading organisations take enterprise data management seriously. Some of the key drivers for this are:

  1. Higher quality enterprise data brings a range of benefits. These range from cost reduction through the avoidance of compliance penalties through to better decision-making with more accurate analytics.

  2. A well-managed data organisation increases data quality and increases efficiency. This is typically achieved by a central Centre of Excellence responsible for maintaining and enforcing data standards and a Shared Service Centre responsible for providing data maintenance services to the wider business. The return on this investment comes in the form of higher data quality and organisational efficiency.

  3. A central data ‘hub’ for managed datasets provides a single source of the truth. By consolidating datasets into systems of record (such as MDM systems), organisations bring efficiency to their enterprise landscape and enable downstream systems to leverage a ‘golden’ dataset from a single point of integration.

Digital brings new data management challenges

Many organisations are undertaking significant transformation programmes to take advantage of ‘digital’. From big data to touchless processes, the benefits of a ‘digital’ are well rehearsed. However, with opportunities come challenges. Some key ‘data’ challenges for organisations are:

  1. The number of data entry points is increasing. Data can enter organisations via a number of means. Take the creation of a customer record as an example. This might come directly from the customer via web site, from a sales representative processing an initial order, from third-party data, such as web crawl or social media feed or via a manual submission through the desktop or mobile UI of an enterprise application such as an MDM solution.

  2. The number of data consumers is increasing. Not only are there more business-users analysing data in more mature ways - for example via self-service data preparation and BI tools - but there are more downstream systems with which data should be shared. This makes it harder to guarantee a ‘single view of the truth’. More integration points bring more points of failure, making it harder to guarantee data integrity across the enterprise.

     

  3. The enterprise data model is becoming devolved.

    The growth of cloud and best-of-breed system brings devolution of functionality from a single core system to myriad satellite systems. This means that the data model underpinning any single enterprise data object becomes more devolved with different systems and business units employing different attributes and measures to achieve their function. This compounds existing challenges such as that of producing a ‘single view of the customer’.

Digital requires a strategic approach

A data strategy is more important than ever. A data strategy should focus an organisation on the next generation of digital opportunities and align these with business goals. It should also ensure that the implementation of new technology is supplemented with supportive organisational structure, policies and processes.

Without a data strategy, there is an increased risk that data-orientated transformational projects will cause long-term problems. Some examples are

  1. The negative effects of poor quality data multiply further downstream – particularly once it has been involved in a business transaction or in a management report.

  2. Data conflict is far harder to resolve after the fact. If the same piece of data is collected from multiple sources and the value differs from source to source it is when two data points exist that attempt to describe the same object but have conflicting values this can be hard to identify and even harder to resolve.

  3. An inefficient data architecture adds to overall complexity and IT cost.

The business case for a data strategy is that it will help an organisation realise the opportunities of a digital transformation at the same time as preparing for the challenges and long-term risks that such projects involve.

 

How to initiate a successful data quality programme

James Blackburn
By James Blackburn

James is a Principal Consultant with a specialism in Enterprise Information Management and Business Intelligence. He is passionate about helping companies improve performance via data-driven solutions and has recently lead a series of data quality projects across a number of global companies.

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