Analytics and Data Science Spend & Trends Report

ANALYTICS AND DATA SCIENCE SPEND & TRENDS REPORT 2022

EXECUTIVE SUMMARY SPEND AND TRENDS REPORT

and Spend , these are ultimately issues that can be solved by the analytics and data science organization. The biggest opportunities are a ‘better understanding of the data from the business,’ ‘adding new tools,’ better data governance,’ ‘democratization,’ and ‘talent.’ In these results, analytics and data science leaders seem to be saying that they see how their actions could influence organizational growth. Infusing the enterprise with understanding, governance and then ultimately democratization through perhaps the adding of additional new tools is the way forward. ‘Data quality’ has been-and will continue to be the focus for analytics and data science leaders. Rather than simply presenting results through visualization tools, the next six months look to feature a greater focus on data integration. Data transformation and augmented analytics are happily big picture, forward thinking initiatives that showcase the analytics and data science discipline playing an integral role in the advancement of global corporate enterprise. The past has featured hard work. The present features the opportunity to engage in the democratization of data and provide analytics inspired insights to business leaders in a way that helps drive business impact and value. The future is bright for those that continue to work and build in the present.

The MADS and All Things Insights community completed an extensive survey covering what folks are thinking, how they’re spending and the issues they face. Our analytics and data science community has a solid core of top-level leaders along with a fair share of ‘front-lines’ folks. More respondents are ‘people managers’ than ‘individual contributors.’ The community is spread across several different industries with a fair share of large organizations represented along with some smaller companies. Respondents feel strongly that analytics and data science are becoming more integrated into both corporate and operational decision making . It’s interesting that there is a near 10% delta in favor of corporate decision-making being more integrated. The difference might have roots in industry distinctions of respondents. But perhaps it also might have to do with operational leaders having to deal with the inherent pitfalls of actual integration . The overt positive is that integration is happening for the majority. Thoughts around the change in the level of influence within the organization over the past two years are less vociferous. That said, there are, of course, many ways to analyze this data point! Yes, over half feel that analytics and data science has ‘increased in influence.’ Which is a positive. But the third of folks who feel that “influence has remained steady” might already have a high level of influence. Stepping back, it’s simply good news that less than 10% of respondents feel that organizational influence has decreased. Working across departments is the biggest challenge for our analytics and data science community. For over a third, ‘Talent/Hiring the right skillset’ is the top challenge. It’s surprising that this isn’t true for 100% of respondents. For just under a third, ‘data quality’ as well as ‘business not understanding analytics and data science’ is the biggest challenge. As noted in Collaboration

Seth Adler All Things Insights

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04 06 08

The All Things Analytics & Data Science Community

State Of The Discipline

Progress made, work to be done by June Dershewitz

Challenges

Opportunities

Data-Led business intuition by Michelle Ballen

22 24 20 26 28 30 12 14 15 16 18

Internal & External Data

Collaboration

Data-driven decision making by Anu Sundaram

Collaboration

Building Consensus by Steve Weiss

Focus

Integration Imperative by Susan Lahey

Spend

Harnessing volatile forces by Omri Orgad

Spend: Last Six Months/Next Six Months

ALL THINGS ANALYTICS & DATA SCIENCE The Community

What title do you hold at your organization?

Are you a people manager or an individual contributor?

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What industry do you work in?

Approximately how many employees are there in your organization in total?

Our analytics and data science community has a solid core of top-level leaders along with a fair share of front-lines folks. More respondents are people managers than individual contributors. The community is spread across several different industries with a fair share of large organizations represented along with some smaller companies.

STATE OF THE DISCIPLINE

Do you feel as though the overall state of the Analytics & Data Science discipline is becoming more integrated into corporate decision-making?

Do you feel as though the overall state of the Analytics & Data Science discipline is becoming more integrated into operational decision-making?

Respondents feel strongly that analytics and data science are becoming more integrated into both corporate and operational decision making. It’s interesting that there is a near 10% delta in favor of corporate decision-making being more integrated. The difference might have roots in industry distinctions of respondents. But perhaps it also might have to do with operational leaders having to deal with the inherent pitfalls of actual integration . The overt positive is that integration is happening for the majority.

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How has the level of influence of the Analytics & Data Science discipline within the organization changed in the past two years?

Thoughts around the change in the level of influence within the organization over the past two years are less vociferous. That said, there are, of course, many ways to analyze this data point! Yes, over half feel that analytics and data science has increased in influence. Which is a positive. But the third of folks who feel that “influence has remained steady” might already have a high level of influence. Stepping back, it’s simply good news that less than 10% of respondents feel that organizational influence has decreased.

PROGRESS MADE, WORK TO BE DONE June Dershewitz unpacks results to showcase not only collective progress but also what needs to be improved and even so thoughts on how to get it done.

The data and analytics organization must be able to communicate with the business in a way that they can understand. And if they’re If data leaders are coming to business leaders with deep technical details - such as, here’s the algorithm we use in our new predictive model - it may not be the best way to communicate with people in a way that will convey value. When I see that “most analytics and data science issues at my organization are caused by data quality,” while I empathize with my colleagues I feel I should point out that the “data quality” issue might

“It’s clear in these results that we still have a lot of work to do to address the potential that we see for using data throughout our organizations.” be the symptom of another problem. For instance, if as a business, there is a certain kind of data that carries a lot of risk with it, and if you have a data quality problem in that data set- that’s extremely dangerous to your company. You’re going to invest in making sure that that data set is high quality and that issue is solved in that use case. I’ve heard stories long ago about companies recognizing that fact. Organizations should be finding risk areas and standing up teams to ensure talent, technology and processes are in place to get those data sets to be as high quality as possible. You can’t do that everywhere. You just need to prioritize and decide which areas carry the highest risk and address them. So to do that for, ”issues caused by data quality,” the question is what’s the impact? Are they small things that are inconsequential or do they carry a lot

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“The gateway to success is making sure that the data you’ve got is made actionable.”

of risk? If they carry a lot of risk, then you need to deal with them right now.

we’re doing is valuable and valued and actionable. I think that seeing these results gives me hope for the future. I do see progress here, but I hope that three years from now we’re not still talking about the same thing. We’ve built up enough sophistication in our practices and our tools; there’s enough available to us to form teams of people with the right tools and platforms and the right tactics to win but we haven’t achieved it yet. Moving forward it’s got to be a joint effort between the data and analytics leaders and the business leaders to agree that this is important and address it. What’s hidden and not revealed here is how hard these respondents actually have been working to get through the business and how that effort is being received by the business. Good business partners need to be identified and brought into this process to close the loop.

The gateway to success is making sure that the data you’ve got is made actionable. We’re not doing all of this work just so that we can stand up tools and platforms and make our data clean and get smart groups of people together. We’re doing all of this work so that we can actually deliver results, and the results are shown in the business picking up the work and taking action on it. It’s clear in these results that we still have a lot of work to do to address the potential that we see for using data throughout our organizations. We have made, collectively, some progress and maybe have some good examples, but we still haven’t completely gotten through to the business that what

June Dershewitz Board Member Digital Analytics Association

CHALLENGES

What are your top challenges? Select your top 3.

Working across departments is the biggest challenge for our analytics and data

science community. Two in five are subject to this point of view. For these respondents, there is the possibility that there is not a C-Level seat at the table for data or that the very top of the organization is not completely ‘bought in’ to the importance of analytics and data science. For over a third, Talent/Hiring the right skillset is the top challenge. It’s surprising that this isn’t true for 100% of respondents. For just under a third, data quality and equally/ respectively business not understanding analytics and data science is the biggest challenge. As noted in Collaboration and Spend, these are ultimately issues that can be solved by the analytics and data science organization.

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OPPORTUNITIES

What are the biggest opportunities within the Analytics & Data Science discipline in your opinion? Select all that apply.

The biggest opportunities are a better understanding of the data from the business, adding new tools, better data governance, democratization, and talent. In these results, analytics and data science leaders seem to be saying that they see how their actions could influence organizational growth. Infusing the enterprise with understanding, governance and then ultimately democratization through perhaps the adding of additional new tools is the way forward.

DATA-LED BUSINESS INTUITION Michelle Ballen dives in on improving data literacy and the issues around data quality. She also discusses how best to educate and collaborate with organizational business leaders.

There’s a lot within these results that makes you feel good. You can see that a lot of the barrier to entry has gone down. You can see that there’s a lot more talent who knows how to work with the data and the importance of it is being communicated. You can see alignment on the way to begin to integrate analytics more into decision making. The fact that data literacy and data quality are top Challenges definitely resonates. You need to add analysts who need to educate business operators about why data governance is important. And supplement that education with an understanding of where poor governance can get in the way of understanding the business so that it can move forward. So data literacy does need to improve across the organization.

“Help business leaders better understand how they can integrate insights or value from the insights they’re provided.”

That said, I don’t really believe in a literacy program. I think you learn by doing real life things that are relevant to you. Having a SQL learning session at your company- I don’t think it’s going to offer much value versus having a weekly working session with an analyst to go over specific data. That way business leaders can better understand how they can integrate insights or value from the insights they’re provided. We need to help business leaders practice and build up an intuition with data sets. And so if you’re joining an organization and you’re in charge of marketing, you need to start building an intuition around the users. Based on the data that you currently have- and everyone’s data is complicated- there’s always so many nuances. The best analysis comes from a partnership between the analyst and the domain lead. Through that partnership,

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“You need to add analysts who need to educate business operators about why data governance is important.”

questions come up which are able to be investigated together, findings can then be reviewed so you can then keep going. And over time the business will become more literate. But you can’t just become literate overnight. You have to spend time. I know. It’s the answer nobody wants. I know it’s not “just buy this tool and it will magically solve all of your problems.” This is a people problem. It’s a process problem. It’s not just a tool problem. Overall, we need to educate better on how the fundamentals inform business growth before we can get quality out of the more advanced solutions. So while we know that the organization doesn’t necessarily excel at data literacy, it also looks like we ourselves in our community might have some unrealistic expectations as well. These results showcase the fact that we all

need to collaborate. While we have expertise and the domain leads have their own expertise. They understand the marketing world, they understand the supply chain world, etc. Bringing our minds together will drive the most value. And we sort of need to hold their hands a little before they can really understand the value of these insights and what to do with them and become self-sufficient.

Michelle Ballen Head of Analytics Future

INTERNAL & EXTERNAL DATA

What types of INTERNAL data does your organization use for decision-making?

What types of EXTERNAL data does your organization use for decision-making?

Customer/Marketing data is used in the decision-making process for nearly two thirds of respondents. For half of the community, their data is finding its way into Operations and Sales decision making. Over a third are influencing finance decision making. And just about a third of R&D and product teams are deciding with assistance from analytics and data science. This demonstrates short-term and long-term decision- making is being influenced .

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COLLABORATION

Most Analytics & Data Science issues at my organization are caused by:

Data quality is causing most issues at respondent’s respective organizations. The lack of needed data is causing issues for just under half of the community. And a third or more are having issues with data access, the inability of the business to understand data and the inability to translate data to the business. As noted in Focus , these are ultimately issues that can be solved by the analytics and data science organization.

DATA-DRIVEN DECISION MAKING Anu Sundaram provides a straightforward analysis of this moment in time in the analytics and data science discipline. She balances that analysis with where we’ve been and where we’re going.

State of Data Science Discipline (Integration into corporate and operational decision making): I am glad to see that the state of Analytics and Data Science discipline is mostly integrated into corporate decision making and continues to be strong for operational decision making. The difference in the response profile is perhaps due to the more strategic nature of corporate vs. tactical actions required at the operational level. For instance, marketing spend optimizations have heavily relied on data science and analytics for day-to-day operations, meanwhile it takes significant effort and alignment between departments for organizational decision making. Overall businesses increasingly understand the importance of data science and analytics in driving informed decisions.

“While insights were a competitive advantage

before, it has become more table stakes now to rely on data driven performance and strategic insights to run the business efficiently.” Collaboration (Marketing & Sales): Almost 81% of marketing and sales organizations say they derive value and are dependent on insights from data science and analytics. Stakeholders from these teams are increasingly integrated with their insights teams, either by having a completely embedded fully skilled team either housed internally or contracted, or tapping into a centralized insights team. While there are advantages of centralized teams that cater to multiple departments, there continue to be benefits of having analysts embedded within business units. These teams typically work very closely with enterprise wide data teams as they rely on each other. Analysts are typically the largest consumers of data and tools built and maintained by enterprise data teams. 70% of marketing and sales teams are well aligned with their data science and analytics teams. This is a testament of how interdependent

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“Prioritization of insight generating work may differ, and finally how the data is processed and interpreted can also be very specific within departments.

these teams are for each other’s success. Early alignment and collective goal setting are key to a symbiotic relationship between these teams. I am not surprised to see high utility (72%) of data science insights by marketing and sales. While insights were a competitive advantage before, it has become more table stakes now to rely on data driven performance and strategic insights to run the business efficiently. Challenges & Opportunities: It is not entirely surprising to see that working across departments is one of the top challenges, even topping hiring/talent management and data quality. Utility and applications of data and insights vary. Prioritization of insight generating work may differ, and finally how the data is processed and interpreted can also be very specific within departments. For instance the understanding of KPIs and insights, the

visual presentation and rigor of analyses are often not at parity. There might also be significant duplication of insights work when multiple analytics teams are embedded into the organization and are trying to answer similar business questions. Now that we have enough or dare I say too much data, better understanding of this asset is a primary opportunity. Global data definitions, strategic prioritization of insights work, self-service tools and cross- functional training can alleviate some of these top challenges. Conclusion: We have come a long way since I started my professional career in data science and analytics. We are moving fast and in a positive direction with better tools, abundant data, cheaper storage and constantly improving ability to process information. Businesses are increasingly reliant on data science and analytics insights to drive decisions.

Anu Sundaram VP, Business Analytics Rue Gilt Groupe

COLLABORATION

How integrated with marketing and sales is the data science organization?

To what extent do you feel that the sales and marketing organization understand and value the insights from data science?

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COLLABORATION

How effectively is the organization utilizing data science insights?

To what extent do you feel that decision makers in sales and marketing organization are aligned with what data science provides?

This is the Somewhat/Fairly portion of this document. It’s possible to look at this positively in that progress is being made. It’s possible to look at this negatively in that we’re not further ahead. We’ll be following-up with the community throughout the year to develop further detail on these answers.

BUILDING CONSENSUS

Steve Weiss empathizes with survey respondents and showcases that the work to be done is in fact difficult. But then offers means with which to make change.

In meeting the challenge of building culture within organizations, we’re still early in the journey. We still have yet to crest that hill in terms of having strong top-down leadership to build acceptance of a unified data culture and then to operationalize it organization-wide. That’s why these responses aren’t a surprise to me at all: You must build consensus in every group and every employee throughout your company. It’s not always easy. There’s both opportunity and threat here. If you’re an organization where many people, for example, in sales and marketing are not in alignment with the strategic work the data team is doing, ask yourself, “Why isn’t one side getting the time to have some

“Data-literate organizations are attractive to, and offer a great path for, people who know how to be an effective change agent.” good, strong conversations with the other?” Has anyone asked the skeptics what they need in order to be convinced? Is it a cultural thing? Or is it the fact that the skeptics are pragmatically saying “If I had better data, if I had stronger reporting and stronger analytics, I’d be able to build success?” This is serious: Being a truly functional data-driven organization is absolutely imperative to your success and survival. You look around the country and around the world and see how many orgs are actually having overt success with establishing data leadership, and the delta between those who are doing it and those who are lacking is opening up competitive gaps. There are a lot of opportunities for education that we measure at LinkedIn Learning. Many enterprises still have a long way to go, but that’s not surprising because when you’re talking about top to bottom organizational change and adoption of new ways of looking at how to do business, it is hard. Every organization, every culture, is a little bit different. Every

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“You must build consensus in every group and every employee throughout your company. It’s not always easy.”

leadership team has their own set of org-centered, idiosyncratic problems that they’ve gotta figure out how to deal with, including opening up data access, embracing a culture of change, and building a philosophy that embraces analytical decision-making. Which may explain why we’re seeing so many people at a senior and executive level interested in this report, because they’re looking for all the help they can get from other people to say how, ‘how are you doing this?’ And they’re looking for help in any number of different categorical places. ‘Is it leadership?’ ‘Is it HR, recruiting, and training?’ ‘Is it engineering and operationalization?’ It’s hard, it’s exciting, and the stakes are incredibly high. The good news is that you can see the needle moving: More companies are making crucial moves like implementing data-fluency training for all employees, making data governance standards strong and understandable, atomizing

data-team structures so that data specialists are everywhere in your org and not just in a high-demand/ underfunded tiger team. HR is increasingly playing a crucial role with org-wide data literacy training and L&D (learning and development) offerings to employees that enable you to hire for individual qualities and then to train or skill-up in various levels of data skills. Because the other huge thing about this, is that there’s a lot of opportunity in turn in terms of jobs: Data-literate organizations are attractive to, and offer a great path for, people who know how to be an effective change agent. Folks like that can write their own ticket, they’re going to be in high demand. Build data literacy into your orgs and hire for data fluency. It you’re not hiring right now due to the economy, start training in-house; skill- up your teams. Now’s the time to build your competitive edge.

Steve Weiss Content Manager, Data Science & Business Analytics LinkedIn

FOCUS

What area of focus has mostly driven Analytics & Data Science within your organization in the last 6 months?

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What are your current areas of focus now? Select all that apply.

As noted in Strengths & Opportunities, the community is saying that their focus is on providing the organization with a data-driven, analytics approved way forward and that they need to bridge the gap of building and democratizing data. There’s also pretty even focus on basic as well as advanced ways forward. These responses do seem to indicate a particular moment of opportunity for the analytics and data science discipline.

INTEGRATION IMPERATIVE

Susan Lahey outlines that the time is now to ensure that analytics and data science are fully integrated into corporate and operational decision-making.

We’re hitting the nail on the head with the responses in this report. It really comes around to, ‘do I have the necessary data available for the study that I’m trying to do?’ And ‘is that good data?’ Data is always gonna be an issue in any type of analytics and data science approach. And the reality is we just have to be able to figure out alternatives. What are the best approaches both from a technical perspective and a sheer modeling perspective and what is going to make the most sense? Data quality is often an outcome from how programs have been executed. When you see data quality being an issue or lack of available data being an issue, it’s important to be able to sit down and really work to understand ways that we can pull the needed data together or piece that data

“It’s important to be able to sit down and really work to understand ways that we can pull the needed data together or piece that data together.” together. Often clients have information. It’s just all in separate areas due to in coming into the organization through different sources. With anything data and analytics based, you’re going in front of people and putting tons of numbers in front of them and trying to build a story along with those numbers. So the real critical piece is making data and analytics more accessible to anyone, regardless of whether or not they consider themselves data savvy. As data and analytics providers our role is to help build that story. We have to understand what are those key critical questions that our customers or clients have and how can we answer those using the numbers that we have. When people say that it’s not as integrated, I don’t think it’s for lack of desire to have it integrated. It’s more

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“We’re hitting the nail on the head with the responses in this report.”

building that knowledge of how to use this information properly and when and where, you can build that into your ongoing planning processes. It’s a good story for data and analytics. Data and analytics is important for all of our clients. They’re saying it’s in their organization and it’s coming top down to have these data and analytics studies to help make decisions. So there is this desire. Survey respondents are clearly saying that analytics and data science should be more integrated into corporate and operational decision-making. That said, it’s clear that there’s the desire for integration and that’s coming top down. But where it gets integrated- the true decision making processes- there may still be work there. If you then take all of the pieces in the middle, is that because data quality is not that great? Is that because there’s too many different departments

that want answers to questions coming out of this? Or is it a talent/hiring issue, whatever it might be? I think it gives us a little bit of insight into the people who want this information. They want data and analytics to help make decisions, but need help with integration. So, data and analytics providers and partners with our clients have to be able to help bridge that gap.

Susan Lahey Senior Manager Ekimetrics

SPEND

Did your Insights budget increase or decrease in 2022?

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Do you plan to increase or decrease your Insights budget in 2023?

The results here show that budgets didn’t go down and that they’re not going down moving forward. So, regardless of the results-no matter how effective or ineffective we’ve been to date, there is in fact enough buy-in from enough corporate leaders on the importance of analytics and data science. Respondents are showcasing though, that more does need to be done. Results need to be shown, outcomes need to occur, and businesses need to grow for the investment in analytics and data science to continue.

HARNESSING VOLATILE FORCES Omri Orgad notes the volatile forces facing society, organizations, insights, analytics and data science teams and provides a path forward for success.

In a volatile economy, corporate and operational decision making are merging into one decision making process. At the time I’m providing this commentary, the EU at 10% inflation rate and the US in a similar state. Such a volatile, fast moving economy demands that the operational and the corporate decision making merge into one. Reaction times must accelerate, so there must be a dovetailing of the tactical and the strategic. And that’s where information is becoming crucial to the core. The influence is increasing. When you have many more decisions to make, then obviously you need to lean on more information. Organizations must find the real data sources and the information within that reflects reality to be able to make real decisions. The job is now to identify relevant data so

“Organizations must find the real data sources and the information within that reflects reality to be able to make real decisions.” as to locate the center of attention of the customer base to be able to assess what is real and what is not real. Data is becoming a tool that is better perceived as a tool that helps make good business decisions. The technology behind it in the market- the data market is definitely going through an improvement. We see a lot of players that are improving in the way that they serve compliant publicly available information. And there’s a lot of work- really, really hard work around it. New tools are being introduced on a daily basis meaning that the market is definitely going in the right direction. We see a better place all the time and it’s something we are happy about seeing better competition. It makes us better. The somewhat/fairly answers are not surprising and not a problem. Inherent in day-to-day sales and marketing

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“These results showcase that professionals are aiming to improve accuracy, decision making and become more efficient.”

are statistics. Every customer you approach or every customer that signs up, or every customer with whom you engage somewhat/fairly will become a customer. That’s the real problem. As we go along the technology will improve in accuracy. Data, at the end of the day, is also a product. Organizations have teams that are not completely aligned. That’s inherent at a large-scale company, but- and this is a big but- sales and marketing teams are now learning to work with data. These results showcase that professionals are aiming to improve accuracy, decision making and become more efficient. We are seeing the effort around those goals. And we’ll likely soon see results. If it’s being more accurate in hiring people. If you’re being more accurate in the go to market, this is the effort in a very volatile economy. There is clearly a lot of effort happening at

organizations around the globe. And I think that if you do the same survey again in six months, you’ll see very different results. Every enterprise that we talk to is aiming to use more information to use it more accurately, to use it faster to handle this very volatile economy.

Omri Orgad Chief Customer Officer Bright Data

SPEND: LAST SIX MONTHS

Which solutions have been the biggest priorities for you in the last 6 months? Select all that apply.

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SPEND: LAST SIX MONTHS

Which solutions do you expect to be priorities for you in the next 6 months? Select all that apply.

Data quality has been-and will continue to be-the focus for analytics and data science leaders. Rather than simply presenting results through visualization tools, the next six months look to feature a greater focus on data integration. Data transformation and augmented analytics are happily big picture, forward thinking initiatives that showcase the analytics and data science discipline playing an integral role in the advancement of global corporate enterprise.