In 2018, every business is now either data driven, or it’s about to be. This means that all business is driven by data and analytics, thus all business users are also data and analytics users. The major challenge of modern business is finding simultaneously finding and providing support for self-service analytics, whilst ensuring security and integrity.
Self-service analytics are meant to empower business users to leverage data independently, with less and less aid from IT or BI teams. But ironically, the self-service process can only be made successful with the help of the latest BI self-service tools and infrastructure, as the traditional BI tools are not fit for self-service support.
So why the rise of self-service analytics? The process can be defined as a simple form of business intelligence (BI), where users in business are empowered to access relevant data, perform queries and generate reports by themselves– with the help of easy-to-use self-service BI tools. The entire self-service process is simplified or scaled down for better usability.
The purpose of self-service analytics is to enable business users to perform their day-to-day analytics tasks themselves and frees up the BI team (having proper back ground in statistical analysis and data science) to get involved in more critical data analysis process.
Despite the fact that most business users now have access to self-service BI tools, only a fraction of those users are able to provide a positive business impact due to issues such as data inconsistency. So how can we manage the data in such a way to reduce chaos and uncertainty, whilst creating efficiency and the desired outcome?
It is obvious that organizations need to be more agile where new data sources and business requirements are concerned, if they wish to remain competitive in today’s market. A great step in this direction is onboarding and levereaging the power of self-service analytics in a correct and conducive manner to data-driven success. There do lie many challenges in this onboarding process however, and it is imperative that the data handling and management is taken care of if employees are going to deploy self-service analytics successfully. There are several, fundamental actionable items which can aid in data chaos digestion. Firstly, make sure you’re starting off on the right foot: the introduction of more powerful self-service BI platforms along with the existing BI tools is imperative if you want to build a strong foundation for self-service analytics. Next, expanding and encouraging the adoption of modern BI tools within each individual business units is key: make sure you’re tailoring your approach to each individual unit for a more precise and nuanced outcome. Precision is valuable both to your organisation and the data user (analyst), as well as the end user. Make sure to implement strict governance to ensure data quality and consistency, as these factors will determine the overall quality of outcome. Not cutting corners here will make sure you don’t have to backtrack in order to re-source the right results. In terms of your analysts and teams, make sure to specifically introduce clear roles and responsibilities where necessary, so that everyone understands what their contribution means to the larger picture. If you give your employees a sense of ownership and accountability, you can be sure that the process will run more smoothly and therefore, more efficiently. Lastly, make sure you do not overlook the power of ‘citizen data scientists’. More on this point below.
The term ‘citizen data scientist’ might be one that you are unfamiliar with if you’re only just beginning to implement self-service analytics, and it is critical that you leverage the value of it. But first– what is a citizen data scientist? The Gartner report defines a citizen data scientist as “a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics”. Essentially, with the help of advanced BI tools and technologies, business users without formal trainging, certifications or degrees in data science, can perform self-service analytics tasks just as well as those with formal training. Of course, the main asset here is manpower: having a larger team enabled with the skills to carry out self-service functions that they could not do prior to the addition of advanced tools.
So let’s get to the risks of self-service analytics… what are the main factors to take into consideration? Addressing these now will help minimise the chance of them during your business operations and processes.
Data inconsistencies
The consistency of your data will determine the consistency and therefore, quality, of your results. This assurance of quality must be carried out prior to the implementation of advanced tools, and before the self-service process begins. Make sure to address different business layers if you want to avoid an inconsistent and erroneous output.
Risk of self-service tools
Following on from above, before the tools are implemented, they must be evaluated for their potential fallacies. Always remember that, despite the automated nature of self-service tools, they cannot be completely relied upon for immaculate service functions and therefore, data results. Make sure to have an assurance process in place to check and verify the tools and their functions as you go.
Limitations of business users
As mentioned above, there is value in citizen data scients– or business users who have no formal training in data science functions. Make sure you appreciate the discrepancy in their experience as compared to real data scientists. Business users have limitations in terms of skills, knowledge and back ground qualifications, in spite of the fact that they’re supplemented by tools and technology. You must make an assessment as to whether your team members will qualify as an appropriate user for the self-service tools. Only after this should you begin training, and make sure you’re tailoring the training methods to each individual and their capabilities. Do not expect each business user to be able to carry out the same tasks to the same level: do your research as a manager so you know what you’re likely to get results wise.
Lack of proper governance
Again, the lack of managerial vigilance and consideration can be a huge risk for self-service analytics. Even after ensuring all the above points, risk is not completely removed unless proper governance is implemented in the entire process. Any loop-holes in the governance process can cause chaos, so make sure that your managerial and senior teams are on the same page before onboarding the rest of your business.
Lack of proper training
In a nutshell: if you train your employees incorrectly or insufficiently, you’re going to get incorrect tor insufficient results.
