Advanced analytics has come a long way in recent years since the hype around big data ignited. Since then, a considerable number of companies have prototyped analytics solutions and sought to operationalize them. Besides data scientists, user groups such as business analysts are now exploring the potential of advanced analytics.
Processes to explore amounts of data and operationalize analytics are being consolidated and advanced analytics tools are increasingly addressing the needs of these different user groups and processes, and even automating data analysis.
Which of these topics will remain relevant? Which experiences are relevant for the future? Which technologies show the most promise? Is advanced analytics still an important topic for the future or is the hype tailing off?
This survey examines the future prospects for the development of advanced analytics in terms of its role for companies, relevant user groups, processes and technologies. Respondents came from large, mid-sized and small companies all over the world, with different backgrounds regarding their use of advanced analytics.
We hope that this article gives you some insight into the current state of advanced analytics and sheds light on its future development to inform your business decisions.
Companies that are using advanced analytics heavily are still an early majority. 20 percent of our survey respondents are using advanced analytics in operational scenarios across different departments. Companies are therefore still able to gain a competitive advantage by using advanced analytics in operational processes. The main barriers to using analytics are a lack of resources such as time and personnel as well as costs and a lack of analytical literacy.
The most important conditions for the successful use of advanced analytics are having the right tool, promoting the topic within the company, training business users in how to analyze data sets and having a holistic data strategy in place. Notably there is a large gap in the importance that best-in-class companies and laggards attribute to investment in training and a holistic data strategy.
These points are not crucial for companies using advanced analytics selectively, but using it across the entire company only works when data access and governance is right and when enough users possess the relevant skills. Improving data management and employees’ skills with training are therefore top investment priorities.
Two thirds of our survey respondents believe that automated machine learning (AutoML) solutions can offer a high level of support in data preparation – the most time-consuming task in advanced analytics projects – as well as in the selection of appropriate models. More than 80 percent see AutoML solutions as a way to make business analysts and data scientists more efficient.
Only a few think that these solutions will replace data scientists. However, expert knowledge is still required to use the solutions and interpret results. Many analytical tasks require human intervention, such as problem formulation, selection of the right method and error measure and results interpretation.
Almost all respondents consider advanced analytics to be a valuable addition to their existing analytics landscape and they also expect business analysts to use it in the future. Data literacy is seen by most as one of the biggest barriers to this. Advanced analytics, especially in operational scenarios, often aims to automate processes. Gaining trust in automated decisions to improve data quality is considered to be a major issue.
Many companies already experience data problems in their business intelligence, so they are aware that input data needs to be reliable and complete. Training business analysts and algorithmic transparency are further areas to address trust in automated decision-making.
Start now
Start now and be part of an early majority that implements and operationalizes advanced analytics across the company. For that to happen, the human resources to devote time to analytics for formulating and testing use cases and making data accessible need to be in place. The benefits are substantial and tangible for those making the effort. Users report benefits with regard to cost savings and improved customer experience.
Get the mix right
Consider blending tools and education to implement advanced analytics successfully. Companies benefit from building awareness about possible use cases and benefits. Employees need to have basic skills in data preparation, visualization and interpretation as well as a shared understanding of the potential of advanced analytics in your business.
Building human resources to improve analytical skills is the most important measure companies can take at an early stage to ensure success. While external resources help to ramp up projects, pervasive use can only be established with broad internal knowledge. Intuitive and business-user-friendly tools facilitate working with advanced analytical methods enormously. Efforts should be accompanied by a clear and pragmatic strategy of how data should and should not be used within the company to inform and automate decisions.
Support your data scientists with AutoML
Use automated machine learning to make data scientists more efficient, especially in data preparation, model training and validation. But do not expect software to replace your data science team in the future. Data science still requires human input, for example, to define the use case, evaluate the results, decide whether to operationalize a prototype and to integrate solutions into the organization.
Advanced analytics will gain further traction in the future – but only when data and analytics literacy keeps up
Expect analytics to be used across different functions in operational processes in the future. Advanced analytics will automate many data-based decisions in future events, but this will only happen when people have gained trust in automated decisions. For this to happen, data and analytics literacy is one of the biggest hurdles. Another key point on this journey is data management. Algorithms are as good as the data they are trained with, so data management and quality are key to ensure algorithms produce valid results.
This infographic highlights 4 key findings from the survey.
Interested in the full study results? You can download them here.
As a Market Research Analyst, Ann-Katrin is primarily responsible for the planning, coordination, implementation and follow-up of market research projects at BARC. She oversees the entire process of creating studies – from designing and programming questionnaires, conducting and analyzing surveys to the layout of studies and infographics on various topics. She also maintains the content of the BI Survey website.
