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.”