As outlined in our previous blog, Data virtualisation and efficiently exploiting the power of the information contained in an organisation comes with its own challenges. In this blog, I’ll outline some of the challenges I have experienced and give some tips on how to navigate your way through if you have similar issues.
Challenge 1 - Where are we?
Like many aspects of IT, Data Management and Data virtualisation are going through their own massive changes. It can be hard to keep up with the latest trends, understand the latest buzz words or work out what to do next.
Many organisations are stuck in the Data Warehouse model and find it hard to see how they can transform. Master Data Management was supposed to get over the issue of legacy data being stuck in multiple legacy systems but is not aligned with the desire to transform to IAAS models. Now there is data virtualization which sounds attractive but can seem daunting from a security, resource and governance perspective.
To keep things simple, you need to know where you are, where you want to get to and how to get there. A short discovery exercise can help to avoid making design mistakes early in the transformation that are difficult to undo later. A maturity assessment can also include a technology horizon scanning exercise that will help de-mystify all of the options available and help you choose which is right for your own situation.
Challenge 2 - What skills are needed?
Gartner identifies one of the top barriers to digital transformation as a talent gap. Marcus Blosch, research vice president at Gartner says, “There are two approaches to breach the talent gap — upskill and bimodal…In smaller or more innovative organizations, it is possible to redefine individuals' roles to include more skills and competencies needed to support digital. In other organizations, using a bimodal approach makes sense by creating a separate group to handle innovation with the requisite skill set."
You can use a maturity assessment to look at the skills you have and what you need. Often existing reporting and business intelligence are ideal for re-skilling and re-training in new technologies and approaches. However, moving away from a traditional data warehouse to a cloud based platform or a SAAS technology provider brings its own unique considerations and should be treated as such. An existing team will need retraining. They nearly always are already in demand and so will find it difficult to dedicate their time to the training and move to the new approach. A blend of existing and new tends to be the way to make the transition in the easiest way bringing the best of the existing knowledge with new technology skills and methods. As the transition is undertaken, a skills transfer exercise can take place. Also a focus on self-service, AI and machine learning will allow more output to be delivered with better use of existing teams.
Challenge 3 - Who are the stakeholders and how to keep them happy?
Data and analytics strategies have moved from “how to drive a successful project” to “how do we use data to make the business more successful.” You need to include your stakeholder needs analysis as part of your data virtualisation strategy.
A long list of stakeholders needs to be considered when looking at the transformation to a data virtualisation approach. The rewards are considerable but the risks of moving when not ready or moving with perceived or real risk can stop a transformation at source. Gartner identifies three steps to securing executive sponsorship:
- Identify the Most Senior Person You Can Engage With
- Create a Common Language Based on Your Organization’s Key Business Goals
- Be Realistic About Your Organization’s Data-Driven Ambitions
It is also important to recognise that ongoing governance is key – you’ve worked hard to transform the way the business uses data but without ongoing governance and management, that hard work can be quickly lost. Senior sponsorship goes a long way here also – buy-in from all areas and a mandate from the top means that teams recognise that this is important and alignment is key. Seeing the MD using the data virtualisation output is a strong validation of your approach.
My Top 5 tips for a successful Data Virtualisation transformation
- Know where you are, where you want to get to and how to get there. If you don’t know then seek advice from someone who has done it before.
- Don’t be held back by history.The technology and approaches have changed to take into account problems and issues you are encountering. Seek similar situations that show how others have solved similar problems.
- Hearts and minds are more important than technology. Successfully engaging with all stakeholders should get more focus than choosing the right technology. Data management, data cleansing and data classification are all important factors that will require ownership from key business stakeholders – these will be factors regardless of the technology choice.
- Sponsorship from the top really helps. Don’t underestimate the power of the MD walking into a sales meeting using business intelligence gathered from your data virtualisation project.
- Governance and security trump everything else. A businesses data is its key asset – it should be treated with respect.