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Identifying Key Trends

Data & AI Survey

Strategy, Organization, Execution

Insights Into How Organizations Achieve Success with Data & AI

Hundreds of professional participate in the annual Data & AI Survey, providing insights into how to achieve success with data & AI. We outline the most important conclusions and identify the top trends and service providers.

Download the Data & AI Survey

Download the 2019/2020 Data & AI Survey and discover key trends and insights in how to achieve succes with data & AI.

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Key Takeaways – The Budget Paradox

Uncover All Data & AI Insights

Key Takeaways

  • Budget paradox – Although only 22% of participants see budget as one of the key elements of a successful data strategy, a minority of 41% agrees they have enough budget for data & AI projects.
  • Business adoption is problematic – A mere 15% of organizations sees their AI applications adopted and used by the business. Building up knowledge and data quality are the biggest challenges in becoming fully data-driven.
  • Python, Microsoft and Tableau are top vendors  – Python is the most popular data technology, Microsoft the number one cloud vendor and Tableau the top data visualization tool.
  • Top trend for 2020 – Taking predictive models into production is the main trend for 2020.
  • Data pros value experimentation, expertise and flexible hours – The top perks for data professionals are the freedom to experiment, team expertise and flexible working hours.

Elements of a Successful Data & AI Strategy

Creating a successful data & AI strategy depends mostly on the organizational and people side of the business. The top aspects for a successful strategy are:

  1. A Clear Vision – Organization – 72%
  2. Support from Management – Organization – 56%
  3. Talent – People – 56%
  4. Support Systems – Technology – 41%
  5. Training – Skills – 33%

Processes (30%) and budget (22%) aren’t deemed very critical for a successful strategy. Some interesting outliers can be found in Telecom, Media/Entertainment and Utilities. For Telecom, the critical success factor is the Supporting Systems (78%), with this factor also scores highly in the Media/Entertainment industry (68%). On the other part of the spectrum Utilities places very little weight on Training, only being indicated as critical by 6% of respondents.

The Budget Paradox

Already mentioned in the introduction, the budget paradox is an interesting finding of the survey. Only 22% of participants see this factor as being critical, while 41% feel they have sufficient budget. This contrast could be caused by budget only coming into view after organizations develop a data & AI strategy.

Positive exceptions for budget are Travel/Tourism/Leisure (67% agree on having enough budget), Utilities (57%) and Professional Services (47%). The sectors struggling the most with budget are Public Services and Education (48% feel they lack budget), Retail (47%) and Media/Entertainment (42%).

Data-Driven Way of Working – Team & Organization

Having a strategy and budget in place is only the starting point for success with data & AI. This is very visible, since only 15% of organizations develops AI applications that are adopted and used by the business. Almost a quarter of organizations don’t do any data science or advanced analytics projects at all.

Most Successful Sectors

The top three sectors when it comes to successfully having AI applications embedded in the business are:

  1. Financial Services – 27%
  2. Telecom – 22%
  3. Travel/Tourism/Leisure – 21%

These industries all have above average scores when it comes to sufficient budget. On the other side of the spectrum we see the same correlation. Sectors like Retail and Telecom struggle with both budget and doing any data projects at all.

There are some interesting paradoxes visible on industry level. While Media/Entertainment struggles with budget, 74% of companies in the sector run experiments together with the business or successfully embed AI solutions. This is well over the 58% average. On the other hand the Manufacturing industry only has a budget issue in 23% of cases, but 33% of organizations don’t do any data projects at all.

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The Most Popular Data & AI Technologies

Most Popular Data Technologies:

  1. Python – 71%
  2. R – 56%
  3. Spark/Apache Spark – 35%
  4. SPSS – 25%
  5. SAS – 21%

On the data technologies front Python continues to rise, increasing from 66% in 2018 to 71% in 2019. With R and Spark the top three remains unchanged for 2019. Cloudera takes a heavy hit, seeing its market share rapidly decline from 22% in 2018 to only 6% in 2019.

Most Popular Cloud Platforms:

When it comes to cloud platforms, Microsoft takes the cake, with almost 49% of companies using Azure in one form or another. Amazon Web Services, private cloud and Google Cloud Platform are separated by very small margins.

  1. Microsoft Azure – 49%
  2. Amazon Web Services – 31%
  3. Private Cloud – 30%
  4. Google Cloud Platform – 29%
  5. Alibaba Cloud – 1%

Most Popular Data Visualization Tools

Just as with cloud platform, it is Microsoft who comes out on top. Power BI is by far the most popular data visualization tool, followed by Tableau and Qlik.

  1. Power BI – 53%
  2. Tableau – 33%
  3. Qlik – 21%
  4. ggplot – 15%
  5. SAS – 13%

Download the Data & AI Survey

Download the 2019/2020 Data & AI Survey and discover key trends and insights in how to achieve succes with data & AI.

I want my copy

Data & AI Trends for 2020

With the majority of companies struggling to have AI applications adopted by the business, is it really a surprise that taking predictive models into production is the top trend for 2020? Of course, productionizing and full adoption are two different things, but the latter can’t exist without the former.

A notable uptake is visible when it comes to using deep learning and neural networks. This is rated as a top trend by 42% of participants, up from 36% last year. With cloud adoption maturing we also see a slight decline in cloud technology being named a top trend. It is down to 20% compared to 21% in 2018.

Top Five Trends:

  1. Taking predictive models into production – 60%
  2. Deep learning and neural networks – 42%
  3. Internet of things – 23%
  4. Cloud technology – 20%
  5. Blockchain – 18%

Attracting Talented Data Professionals

What do data professionals value when choosing an employer? It’s not a company car, which ranks last for the third consecutive year. The freedom to experiment trumps again, with an 8.2, followed by team expertise and flexible working hours.

Top Ranking Factors for Attracting Talent

  1. Freedom to experiment – 8.2
  2. Team expertise – 7.3
  3. Flexible working hours – 7.3
  4. Transparent organization – 7.1
  5. Salary – 6.7

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