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1、The Data Stack Evolution:Legacy Challenges and AI OpportunitiesTable of Contents03040506081011141616IntroductionExecutive summaryKey insightsCurbing enthusiasmReady,set,genAIBudgeting for the futureGetting the balance right people and technologyThe death of the spreadsheetConclusionAbout the researc

2、hData is a powerful engine of business innovation.Forty-five percent(45%)of the respondents we surveyed are using it to achieve faster,more confident decision-making;44%to improve customer satisfaction through personalization;43%to formulate new products and services.But,are businesses over-confiden

3、t about the quality of their data?As game-changing new technologies emerge,are organizations data-ready?Or are legacy data stacks,traditional budgeting practices,and disconnected data team structures holding businesses back in a constantly changing technological landscape?Led by insights from IT dec

4、ision-makers on data leadership teams,we examine how global businesses are managing their data stacks and discover answers to key questions,trends in data team practices,and predictions about which data stack technologies are set to rise and fall.Introductionuse data to achieve faster,more confident

5、 decision-making45%use data to improve customer satisfaction through personalization44%use data to formulate new products and services43%3 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES Executive summaryAs organizations become increasingly reliant on data-driven systems to run nearl

6、y every aspect of the business,the quality and availability of their data take on monumental importance.Add to that the pressure to adopt AI-based technology that is only as good as the data its trained on,and the criticality explodes.But the surprising truth,as revealed by our research,is that the

7、majority of companies are still relying on legacy technologies where their data is concerned.Many organizations are facing a significant challenge in the form of data debt(where a business is not proactively managing the quality of its data,resulting in a negative impact on costs and productivity).A

8、s a result,organizations are experiencing large-scale data wastage,an issue compounded by the siloed operating nature of their teams.The solution is complicated:a striking finding from the research shows that data teams dont have control over how budgets are spent,and many are held to fixed budgets

9、that do not allow them to respond flexibly to technological developments.In the midst of the generative AI revolution,these restrictions alongside widespread budgetary pressures are preventing businesses from staying ahead of the curve.An additional worrying observation is that across the business l

10、andscape,companies cant reach a consensus on who ought to be the final interpreter of data within an organization.Our research shows that many organizations are taking an employee-centered approach to data.When upgrading data stacks,employee expertise and satisfaction are significant considerations.

11、Finally,as data leaders make predictions about the immediate future of their data stacks,we see the use of AI growing and the use of spreadsheets shrinking.Perhaps the duct tape of the data stack is set to be replaced by the technology thats changing everything.4 THE DATA STACK EVOLUTION:LEGACY CHAL

12、LENGES AND AI OPPORTUNITIES Key insightsof companies have a data stack that could be classified as modern.10%of IT decision-makers say that they have strategies in place to reduce bias in their data.15%of organizations keep data within the department that has generated it it isnt shared across depar

13、tments.48%of data teams say that their budget is not reviewed or adjusted throughout the year.56%of survey respondents say that more regular training refreshers would help data tools and technologies become more accessible to people in their organization.61%of respondents named decreased employee sa

14、tisfaction as a key outcome of limitations in the data stack.19%respondents predict that data stacks will evolve,with the proportion of data science and AI platforms in data stacks expected to grow by 7%and genAI expected to grow by 4%.Over the next 3 yearsof companies have the board of directors as

15、 the final owner of data.11%76%of businesses trust their organizations data without reservation.5 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES Curbing enthusiasmBusinesses are very optimistic about data quality.Should they be?When asked,most businesses report that they are satisfi

16、ed with the quality of their data.Across various measures,they feel its serving them well:79%rate their data highly for discoverability,74%for consistency,73%for analytics trust,70%for completeness,and 69%for integrity.Businesses say that their data is getting results.Seventy-six percent(76%)state t

17、hat they are satisfied or extremely satisfied with the value data delivers in their organization.Broadly,where data is concerned,companies feel that theyre getting it right:over half (54%)rated their own data maturity as good or advanced.Seventy-six percent(76%)trust their organizations data without

18、 reservation 31%say they have high trust in their data,and 20%say they have complete trust in it.(Figure 1)IT decision-makers on data leadership teams feel good about their data technology investments.They report that the data technologies they use are solving common challenges faced by data teams.F

19、or example,77%say that the data technologies they use contribute to solving problems posed by the inability to document,govern,or observe data pipelines.Seventy-three percent(73%)state that they help to address problems caused by difficulty accessing data.Seventy-two percent(72%)believe that they he

20、lp to counteract data bias.Figure 1-What level of trust do you have in your organizations data?6 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES However,is this general feeling of optimism rooted in fact?Digging deeper into industry sentiment,some doubts emerge.When it comes to makin

21、g decisions in their organization,one in five respondents (22%)say that data bias is one of the most critical challenges they face,and 20%flag poor data quality.If we explore whats giving rise to these issues,we start to see a more realistic picture.When companies reveal more about their data stack,

22、it turns out that only 10%have a data stack that is classified as modern.(Figure 2)Forty-three percent(43%)have a hybrid data stack,combining modern and legacy technologies,30%rely on a legacy data stack,and,for 17%,their data stack is in transition from a legacy set-up to a modern stack.(Figure 2)T

23、he prevalence of legacy data technology across the majority of businesses is also indicated by the average proportion of their budget that IT departments are currently spending on maintenance over a third (34%).So,while IT decision-makers feel optimistic about the quality of their data,90%are relyin

24、g on data stacks that are out of date.And this indicates theyre missing out on some level of quality.In fact,23%say that they hope to get much more out of their data quality by continuing to invest in data stack technology.Figure 2-Would you classify your organizations data stack as:7 THE DATA STACK

25、 EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES Ready,set,genAIIs data debt preventing businesses from exploiting new technologies?As generative AI transforms industries across all sectors,companies aspire to move quickly to exploit its extraordinary possibilities.However,the truth of the matter i

26、s that genAI is only as good as the data that feeds it.As they seek to take advantage of this powerful emerging technology,the problem that many businesses face is data debt.Data debt is what happens when a business doesnt catalog,clean,and categorize its data.This technical debt drags down producti

27、vity and overall business performance.The results of our survey suggest that companies may not be optimizing the quality of their data.When asked which data governance policies they have in place,only 15%say that they have strategies in place to reduce bias,and only 27%say they have strategies in pl

28、ace around data cleaning and pre-processing.Most organizations,it seems,are not adequately keeping tabs on their data.One issue seems to be that organizational structure isnt consistently set up to maximize the investment in data.In two in five companies (41%),theres no centralized data/analytics fu

29、nction that maintains data as a shared resource for the business.(Figure 3)Instead,different departments and business units manage their own data.In almost half of companies (48%),data is kept within the unit that has generated the data it isnt shared across departments.(Figure 3)These statistics dr

30、ive home the fact that across many businesses,data isnt being used efficiently or accessed by the relevant teams.Figure 3 Choose the statement that best describes the usage of data in your organization.8 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES As technologies advance,the ques

31、tion data teams should ask themselves is whether their business is set up to make the most of internal data.Companies who want to move forward with AI especially localized or proprietary internal models must make sure that their data is ready to be utilized on a massive scale and with impeccable qua

32、lity and governance.Do they know how to deal with large volumes of data?Are they managing it actively?Are they analyzing it effectively?Or,is there large-scale data wastage?For those companies who respond to these questions with uncertainty,new,efficient technologies may offer the secret to reducing

33、 data bias.Sixty-nine percent(69%)of companies say that they are leveraging technology to combat bias,while 76%rely on technology to clean and pre-process data.This is interesting,in light that only 15%state they have strategies in place to reduce bias,and only 27%have strategies in place around dat

34、a cleaning and pre-processing;does this suggest that technology is being used without a strategy in many businesses?Its likely worth investigating,especially when only 10%of businesses state they have what can be classified as a modern data stack.At the moment,not everyone is satisfied with their da

35、ta stack.Nineteen percent(19%)say that limitations in their stack may cause them,or have already caused them,to rely increasingly on outdated or legacy technology.When considering their future data stack,organizations say that their top priority is to improve data quality.(Figure 4)Figure 4 What do

36、you consider to be your organizations top three priorities for its future data stack?9 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES Budgeting for the future The future is coming.Are budgets getting in the way?Strikingly,for over half of organizations,budgets,once decided,are fixed

37、 for the year and will not be adjusted.Fifty-four percent(54%)of decision-makers say that even if other priorities,projects,or spending needs arise,they are held to what has already been allocated.Fifty-six percent(56%)say that budgets are not reviewed or adjusted throughout the year.Frequently,then

38、,budgets within data teams are not adjusted to match how a given year shapes up.Its illuminating to consider that in a year when emerging AI drove widespread,extensive change,organizations with fixed budgets were not in a position to respond flexibly and,thus,keep up with these developments.In gener

39、al,IT teams are responsible for holding the budgets for data technology.For example,the budget for genAI is held by IT teams(64%)and,to a lesser extent,by business leaders(47%).Only 9%is held by business users/individual departments.(Figure 5)Figure 5-Who within your organization would you consider

40、has the most responsibility for the daily running and maintenance of these types of data technology?10 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES Getting the balance right people and technologyWhos really in charge of a companys data?Its clear from our data that in many business

41、es,data isnt shared with everyone.So,who typically owns it and may be contributing to that decision?Across most of the businesses we surveyed,the Chief Data Officer(22%)is the ultimate owner of data,but in 11%,data is owned by the board of directors,and in 8%,by senior executives.(Figure 6)Meanwhile

42、,the way organizations are choosing to structure themselves suggests that they know how valuable their data is and how critical it is to the success of a business.In nearly one-third (29%)of businesses,the IT team sits within the overall data leadership team rather than the other way around.(Figure

43、7)Across all businesses surveyed,this is the second most common way of structuring the two teams.Running counter to what we might assume about reporting lines,it emphasizes that many businesses see themselves as firmly data-led.Figure 6-Who manages data within your organization,and who is the ultima

44、te owner?11 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES When we examine the responsibilities of the IT team and the data leadership team,they initially look similar,with both teams considering data quality management their top responsibility.However,the IT team is more likely to

45、focus on data engineering(44%of respondents see this as ITs responsibility),while the data leadership team focuses on organizing data to solve business problems(45%identify this as the data leadership teams responsibility).One notable difference between how the IT and data teams operate is that IT l

46、eaders are the most likely people in an organization to influence technology purchases for the data stack,at 40%.Data leaders are far behind them,at 17%.Intriguingly,board members come in at 21%,and the C-suite at 11%.All in all,it seems there is no real consensus on who ought to be the final owner,

47、interpreter,or investor where data is concerned,though IT is the most common owner.Are businesses fitting tech to people,or people to tech?Seventy-one percent(71%)of decision-makers state that their businesses can attract,retain,and develop employees with the right skills for their data stack.But it

48、 seems that in many businesses,the relationship between people and technology doesnt just go one way.In fact,respondents arent just thinking about how to hire people who meet their technological needs,but also how to develop their existing employees to take on new technological challenges.When think

49、ing about the future of their data stack,one in four employers(25%)consider it a top priority to invest in employee training and development.When asked what needs to change for data tools and technologies to become more accessible to people in their organization,Figure 7-What is the relationship bet

50、ween the data leadership team and the IT team in your organization?12 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES 61%say that organizations need to provide more regular updates and training refreshers.Over half(51%)say their organization should establish/increase training or educ

51、ation.Theres a focus on developing the skills of employees.In fact,one in five companies(22%)go so far as to say that the structure of their data stack is shaped by in-house technical expertise.According to decision-makers,technical expertise,available data sources,and existing IT infrastructure(all

52、 22%)are the top three drivers determining the structure of data stacks coming out very slightly ahead of business objectives at 21%.(Figure 8)Many employers have their workforce in mind when they make decisions about their data stack,with one in five(19%)anxious that data stack limitations will hav

53、e a direct negative impact on employee satisfaction.Figure 8-What are the top 3 drivers determining the structure of your organizations data stack?13 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES The death of the spreadsheetWill this data management staple finally bite the dust?For

54、 many years,spreadsheet software has been an unchanging component of data stacks considered reliable and accessible,if not exactly the height of sophistication.According to our survey,the top three components in current data stacks are Customer Relationship Management(CRM)software (41%),Enterprise R

55、esource Planning(ERP)software(35%),and spreadsheets(35%).However,when asked to project three years into the future,respondents predict that things are beginning to change.The top three components in 2026/27 data stacks are believed to be CRM software(43%),data science and AI platforms(40%previously

56、at 33%),and ERP software(39%).Spreadsheet software has made a small but significant drop to 30%.(Figure 9)Perhaps,in a new technological era,this old favorite may finally be on its last legs.Figure 9-What is the composition of your data technology stack currently?And what do you expect your data tec

57、hnology stack to look like in 3 years time14 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES Over the next three years,the proportion of data science and AI platforms in data stacks is expected to grow by 7%,and genAI is expected to grow by 4%.It may be the case that data leaders hav

58、e finally found something they believe will be an adequate replacement for the classic cell-based ledger that,in the future,they see genAI taking over from the faithful spreadsheet.Its interesting to note that when considering which technology to invest in as part of the future data stack,organizati

59、ons prioritize cost(30%)and ease of use(29%).(Figure 10)These are the top two criteria that businesses apply to data technology investments as they move into the future.So,when making predictions about the future shape of data stacks,the two questions to ask about any new technology are these:is it

60、accessible?And is it a good value for money?At the moment,genAI seems to fit the bill.Figure 10-What factors are important when considering a new technology to invest in as part of your data stack?15 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES How will the tools and technologies

61、you use in your data stack impact the future of your company?If,like nine out of ten businesses,you are relying on legacy data stack technology,you may be limiting your potential for growth including your readiness for the transformational possibilities offered by genAI.Your investment will determin

62、e how quickly you can make accurate business decisions,how many people can leverage data,and adapt to new technologies.Its also worth considering if your budgeting practices are flexible enough to keep pace with rapidly evolving technologies,now and in the future.Perhaps its also time to think about

63、 who within your company is shaping the interpretation and use of data,and who oversees future data stack investments.As many companies invest in upskilling employees in data technologies,its a good idea to ask what training might be useful to your team and identify the in-house skills that will hel

64、p you develop your data stack productively in the future.GenAI also looks set to change the shape of data stacks and is likely to pioneer a deeper transformation of businesses data deployment in years to come.ConclusionThe survey was conducted by Coleman Parkes from November 2023 to January 2024,and

65、 targeted 3,100 IT decision-makers on data leadership teams in organizations across key sectors in the Americas,EMEA,and APJ regions.Survey respondents worked in financial services,the public sector,manufacturing,and technology for organizations with global revenue ranging from$50 million to over$10

66、 billion.The smallest employed a workforce of 500 1,000 people,and the largest had more than 10,000 employees.About Coleman Parkes Coleman Parkes is a full-service B2B market research agency specializing in IT/technology studies,targeting senior decision makers in SMB to large enterprises across mul

67、tiple sectors globally.For more information,contact IanBestoncoleman-parkes.co.uk About the research16 THE DATA STACK EVOLUTION:LEGACY CHALLENGES AND AI OPPORTUNITIES Alteryx is a registered trademark of Alteryx,Inc.All other product and brand names may be trademarks or registered trademarks of thei

68、r respective owners.About AlteryxAlteryx powers actionable insights with the AI Platform for Enterprise Analytics.With Alteryx,organizations can drive smarter,faster decisions with a secure platform deployable in on-prem,hybrid,and cloud environments.More than 8,000 customers globally rely on Alteryx to automate analytics to improve revenue performance,manage costs,and mitigate risks across their organizations.To learn more,visit .