Wavestone: Artificial intelligence- An increasingly complex operational challenge

Article Synopsis :

Organisations face two key challenges over data security. The sheer volume makes the adoption of artificial intelligence (AI) technologies absolutely critical for businesses that wish to remain competitive.

AI is critical for the delivery of  effective cybersecurity

However, regulators are placing increased emphasis upon controls to ensure that consumer data is protected.

The internet of things (IoT) is partly responsible for the exponential growth of data and with the number of connected devices expected to exceed 20 billion by the year 2025.

In order to compete, businesses need to be able to manage the vast amounts of data being generated. This is where modern versions of AI, such as machine learning, RPA among other disciplines are driving the fourth industrial revolution.

Data will increasingly be seen as an asset that provides insights to drive business change and they will tend to sit on data in order to exploit it at a later date.

Datastores such as these have raised concerns about security and are coming under increased regulatory scrutiny.

Data is a global regulatory focus

It is not only recently that data has been a major concern for regulators, says this Wavestone report. The first data privacy law was introduced in Sweden in 1973. The UK followed, introduced the Data Protection Act 1998 that made the misuse or mismanagement of personal data unlawful.

The current gold standard for these controls is the EU’s General Data Protection Regulation (GDPR).

Solvency II has made premium data management as an essential component of operating in the insurance industry, significantly changing the way data used to calculate risk is collected.

‘Know your customer’ (KYC) has been adopted across the world to inform local regulations on money laundering and other financial matters.

GDPR has made data security a serious matter. Facebook has been fined $5 billion for its handling of data when working with Cambridge Analytica.

In Europe, fines can now be up to 4% of annual turnover or 20m euros. In the UK, British Airways faced a record £183 million for a data breach.

These regulations are influencing how businesses interact with client and partner data. Knowing who you’re doing business with and ensuring their data remains secure is no longer important, it is imperative, as written into legislation.

And yet, the data keeps building, forcing the implementation of AI technologies. Yet, the laws governing how these can and should be used is in a state of flux, though tending towards greater restrictions.

Building the right framework

Working out what needs to be done first or most is a major task in itself, but Wavestone says there are four key points that provide the data framework for successful implementation. These are:

• Data ambition –data should be a core part of the business and managed like other key assets, under the control of a data officer.
• Data delivery – a good data strategy is influenced by the quick and successful delivery of data. Ensure the first product or service produced from the data factory demonstrates real value either in terms of operational performance or profit. Build automation and longevity at the core of each service or product delivered. This will reinforce the benefit of a good data strategy in the executive committee.
• Data organisation – this is paramount for a successful data strategy. It requires bottom-up collaboration, a key champion on the executive committee and independence between traditional IT and data officer. This encourages quick decision making by the executive.
• Data governance – this must be determined by the use case, not seniority. Combined with bottom-up governance means that data scientists – or those closest to the data and with the best knowledge– are the ones making or influencing the decision making.

Once implemented, the next challenge is to build an IT infrastructure that can support these technologies.

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