Five key priorities for data leaders in 2023
The world of tech is moving faster than ever before, with great leaps forward in the fields of artificial intelligence (AI) led by the likes of ChatGPT and other generative AI platforms. At the same time, the volume of data businesses and their customers create is increasing significantly.
But how should data leaders navigate the unique challenges 2023 brings? What should be the key areas of focus for the year to come? Here’s what the experts advise become key priorities.
1. Consolidation of technology
As the panic of the pandemic begins to settle into some semblance of normality, businesses are able to take stock of the emergency action they put in place at the peak of the outbreak. “In the current economic climate, lots of organisations are really thinking around optimisation,” says Robin Sutara, field CTO at Databricks. “It’s becoming too complex to manage multiple ecosystems and multiple platforms.” Sutara says she believes that it’s incumbent on data leaders to figure out how to bring down budgets and make processes simpler on a technology level, while ensuring that their business continues to operate without interruption.
“Lots of organisations are starting to realise that managing and running teams – and trying to drive an organisation to use multiple tools and multiple capabilities across those toolsets – is becoming very complex,” says Sutara. A fragmented approach can cause problems, suggests Sutara’s colleague, Dael Williamson, EMEA field CTO at Databricks. “Fragmentation causes a huge amount of plumbing necessary to figure out the way forward,” he says. “What you end up seeing is data copied back and forth across the organisation as a transformation tax.”
The consolidation doesn’t just stop when it comes to data. “As workforces have moved to hybrid and remote setups, there has never been a greater need to consolidate the cybersecurity of the networks they use,” says Graham Smith, head of customer success at cybersecurity specialist OryxAlign. “The more devices that operate on a network, the less effective traditional endpoint threat detection becomes.”
2. Improving data quality
Bad habits become entrenched quickly within businesses, and Williamson worries that the old, inefficient ways of working can store up issues for the long run. Passing data back and forth by hand can cause problems. “There are potential quality issues that can creep in,” he says.
Having poor-quality data within a business can be a headache that balloons into a major problem, not least because of the potential to use that data to train AI models that are meant to bring efficiency into an organisation.
One of the major issues with AI is a common maxim: garbage in equals garbage out. If low-quality data is used to train AI models, it can cause havoc by producing low-quality outputs that are then used to make decisions. Cleaning up the data you do have, and making sure it’s informing decisions in a logical way, is important given it can have wider ramifications for the running of an organisation.
3. Enabling data democratisation across the organisation
Williamson says there’s a worrying habit he encounters with companies when Databricks is asked to help with their data management and digital overhaul. He’ll enter a business that touts its amazing new app that’s meant to help employees do their jobs better. But when he goes into an organisation, he’ll speak to the team it’s been designed for. “They were completely unaware that this platform was being built for them,” says Williamson. “Now suddenly, it’s been thrust upon them. And they’re expected to kind of hit the ground running.” Giving employees more information to assist them in their work is a good thing, but that rollout must be thought out carefully and come with an education piece.
Democratisation has another benefit, says Chris Gorton, senior vice president and managing director for EMEA at Syniti. “When people don’t trust the data, there’s a problem,” he says. “It can mean bad decisions are made or simply not made at all.”
4. Developing a clear governance strategy
As data becomes ever more important and valued to businesses and individuals, governments and regulators are beginning to recognise the importance of policing how that data is handled. Fragmented ecosystems cause problems when it comes to who controls data, and from where it’s sourced, says Sutara.
“Organisations this year are really going to have to think about simplifying their governance strategy across their organisation,” she says. “Do you really understand the lineage of your data? Do you really understand who has access to it? Do you know [this beyond] just tracking on an Excel spreadsheet or a Word document?” As regulators become more cognisant of the concerns around data and beef up their regulations, the onus is on businesses to prove they have control of the data and can be clear about where it’s coming from.
5. Doing more with less without alienating employees
It hasn’t escaped anyone’s attention that the world economy is particularly challenging right now. Businesses are being threatened in a way that they haven’t before, with high competition increasing the risk of getting anything wrong. The rise of AI offers a solution to that problem, making things more efficient. Research from Rackspace Technology suggests that in 2022, 50% of organisations wanted AI and machine learning to help with improved speed and efficiency. Now, 70% of businesses are seeing the benefits.
“There’s a huge amount of concern around ‘AI is going to steal my job!’,” says Williamson. “I don’t think that that is a valid concern in the same way as a photocopier doesn’t take everyone’s job away.” Instead, it’ll make people more efficient. But communicating that to a nervous workforce watching headlines about AI’s revolutionary power is challenging.
“It’s a battle to get there,” says Sutara. “Oftentimes, organisations are focused on the technology and not on the people in the process.”