Data Vault 2.0 is gaining traction as a method for the design and build of Data Warehouses but, as when you try anything for the first time there are some potential pitfalls. We have seen some issues our clients have faced, and we thought we would share three common issues and some tips for avoiding them.

Do you have problems with data? Do you or your team spend time correcting or fixing data?

<p>On the day the GDPR regulations came into force the UK regulator, Elizabeth Denham, told the BBC that enforcement would focus upon companies that &#8220;deliberately, persistently and negligently misuse data and that&#8217;s going to be the focus, that&#8217;s what people expect us to do&#8221;. In the same interview she implicitly recognised that things were not going to be perfect on day one.</p>

It does not matter whether you are running the country or running a business, one of the key challenges facing any organisation is to bridge the gap between policy and execution. Policy makers can sit in an ivory tower crafting well-thought through ideas into detailed policy, but it counts for nothing unless it can be executed meaningfully on the ground.

Single Customer View projects traditionally seek to create a single source of truth about customers and their touch points with an organisation. The project attempts to line up data from all the different sources in the load process assuming that customer data will form one tightly integrated result.  It all sounds straightforward &#8211; surely customer data is the same whatever the source?

It is one thing to write a set of Data Protection policies and procedures.  It is another to get your staff to follow them.  The key to effective GDPR compliance is making sure people understand what it means to them day to day in their own jobs.

Privacy notices on websites are nothing new. In 2010 Facebook managed a level of notoriety when its Privacy policy was found to be longer than the US constitution. This is exactly the type of thing that GDPR sets out to tackle so I thought it might be worth looking at what it means to your privacy policy.

Business Intelligence (BI) architectures today need to provide much more than the overnight canned reporting and dashboarding needs of the traditional system.

In an age of Big Data and Artificial Intelligence profiling using personal data is inevitable. Profiling is automated decision making based upon the data you hold about an individual. The accessibility of the data and ease of processing mean that more and more organisations will start using profiling.

The increasing prevalence of information/data governance structures within organisations is breeding a new function.  The role of the Business Data Steward is frequently being defined by an information governance methodology but it needn’t be so.  Even low key data governance or master data management (MDM) initiatives require the same Stewardship tasks and accountabilities to be performed.

Data Stewards help resolve the perennial question who “owns” the data.  Discussions about ownership are probably unhelpful because they imply the ultimate form of responsibility and accountability for data.  One party or another is trying to exert influence or avoid accountability.

The role of the Data Steward is complex and interacts with many different parts of the organisation and ties together many important aspects of data management. In many ways the Data Steward is at the sharp end of Data Governance, where policy meets action.