A blog about how analytics affects the culture of architecture, ethics and values.

This little sticker was put on my screen as a farewell salute by a now former colleague on her last day in the office. Most organisations talk about how to build strong cultures or change the current cultural perception to match the new digitalized landscape, which is not necessarily an easy task nor done overnight.

I don’t hold the silver bullet, but I know quite a few expects, who are open to share their thoughts. Where and how? Go to – or actually read though this post to get to - the bottom of this post for more details.

Farewell salute from @kristinechris You are so right. #life at @SASsoftware pic.twitter.com/eXHe1wgZoU

— Per Hyldborg (@phyldborg) September 26, 2016

We like to talk about analytics culture. It helps describe advances and challenges an organisation faces when exploiting insights from data.  “People, process and technology” truly reflects the requirements for practically any data challenge; this sentiment encapsulates the case review by Colin Powell at one of our events last year.

With analytics based decisions blooming in many more parts of the organisation, it is becoming clear that cultural evolution is being buoyed, and in some cases pushed.

EU General Data Protection Regulation (GDPR)

Arguably, nothing focuses the change-resistant parts of an organisation faster than looming regulatory compliance. In less than two years, all organizations handling EU citizens’ personal data must comply with the new EU General Data Protection Regulation (GDPR). The deadline is May 2018, and noncompliance will cost up to €20 million, or 4 percent of your annual global turnover! The impacts on how data is collected and processed are significant, and 700 days is very little time to overcome the complex challenges ahead. The number of inquiries rolling in to my colleagues in the data management practice is a good indicator of how seriously this issue is being taken.

Machine Learning for Artificial Intelligence

As we use analytics to drive innovation in our organizations and in society, it is essential to realize that data science today is very much shaping the way we will live and work with machines in the future. The incredible potential of machine learning in a number of business contexts are framed only by the technical and ethical cornerstones of future artificial intelligence.

I especially like the way Andrew Pease describes the dance between data science and artificial intelligence. "Learning to dance with this new partner will be a delicate balance of directing the algorithms (through informed feature selection, feedback loops, manual model parameter selection and business rule encoding) and letting them lead (through autotuning, optimization techniques and deep learning). However, one area where we, as data scientists, most definitely need to take the lead is in developing and using ethical frameworks. Computers are getting better at simulating intelligence, but so far, lag in simulating human values."

IT function as enabler of maturing practices

Standalone business process-specific analytics programmes typically staff up or engage external specialists to help navigate data management, modelling and reporting. But with increasing awareness that data access and insights development across organisations, and with 3rd parties, is necessary, there is growing need for a more holistic approach. Add to that the regulatory and moral considerations described earlier, and we have momentum for an enterprise analytics architecture.

IT has an integral role to play in helping enterprises become more analytically driven and competitive in the marketplace. Business users may have a vision of what they want to achieve but it is the IT organization that needs to deliver the platform for analytics, provision the data for discovery work and produce an operating model that can run a production service for the business. Which is why our experts from the Global Technology Practice are teaming with Intel for a session on the changing nature of Architecture for Analytics that will explore modernisation, deployment strategies and the emergence of ‘analytics competence centers’ within IT.