Master Your Data with SSA (Self-Service Analytics)


Data Science as part of the Digital Transformation 


By embracing Digital Transformation, companies must undertake a transformative journey to adapt their processes and systems, to completely leverage the potential of the available innovative technologies, as well as the potential of the data they generate daily. Data Science as a whole is essentially the “cherry on top” of the digital transformation, supporting the data-based decision-making process, which is more than necessary in today’s ever-increasing competitive world.


Considering this, data science emphasizes that companies must know and understand their data, as that is the only way to thrive in the competitive market and learn from data-driven decisions. The Self-Service Analytics paradigm supports this transformative process, providing an opportunity for making impactful and data-driven decisions based on real-time data fully autonomously.


Definition of Self-Service Analytics 


In the changing field of data analysis, adopting the Self-Service paradigm enables business users in all departments to autonomously use the data they have. This approach promotes data democracy, by making it accessible to a wide range of employees, including executives, managers, and frontline employees.


Self-service Business Intelligence is becoming a vital part of the approach to overall data strategies of all companies, regardless of their size. Organizations can increase productivity, performance, efficiency, data literacy, and ultimately revenue and profits when teams have access to trustworthy and accurate KPIs presented to stakeholders and decision-makers.


Benefits of Self-Service Analytics


The implementation of self-service analytics brings numerous benefits to organizations, including:


Extract high-value from your data completely autonomously: By delivering the right solution, with a little training and the right tools, business users are able to perform ad hoc analysis and create their visualizations independently. This allows them to focus on more strategic and complex analysis rather than on data preparation and correlation in order to make a design or analyze certain behaviors. This analysis will essentially help the users to have the right focus and to efficiently use their time.


Implement data-driven decision-making in a fast and efficient way:  Self-service analytics eliminates the bottlenecks associated with traditional BI processes, where business users have to rely on a limited number of reports for data analysis. This methodology helps businesses address the biggest challenges, such as redundant reports, and data-driven business decision-making processes.


Focus your management toward business success:  Organizations that effectively leverage self-service analytics can gain a competitive advantage in the marketplace by extensively focusing on their own data and making the most out of it. By following this standard, companies can analyze emerging trends in real-time, customer preferences and behavior, and market opportunities, allowing them to make strategic moves ahead of their competitors.


Get a scalable and flexible long-term solution: Self-service analytics solutions are easy to scale as they follow the basic business ontologies and business practices. As the amount of data that businesses are collecting is growing exponentially, the Self-Service solution is scalable toward various company hierarchy levels, going back to the lowest one.  


Training and mentorship from proven domain experts:  At Data Masters, our approach is centered around elevating your team’s data knowledge and creating internal Data Masters. We understand that each organization has unique requirements and varying levels of expertise. That’s why we offer tailor-made solutions that are designed to meet your specific needs and improve the professional qualifications of your team members.



How to implement Self-Service Analytics


Excel is without a doubt the most popular tool for Data Analysis so far, but it’s not without limitations. Advanced users can perform many complex tasks in Excel, such as manually entering data, performing ad-hoc checks and corrections, and using pivot tables to analyze data from a predefined table as a source. While this way of analysis works perfectly when having a dataset not expressed in millions of rows, working with bigger datasets becomes a real challenge.


Working with Excel becomes a bit easier when the company already has a Data Warehouse, and the main rules, data cleaning, and transformation are supposedly already done in the DWH. However, over time as the maturity of the company grows, the main question that started arising for each company is not whether there is a Data Warehouse or some BI structure, but how independent the business users are from the DWH/IT department and how quickly they can get the information needed for reporting or for decision-making.


The worldwide need for specialists to satisfy the need for ad-hoc analysis and not only to provide tools for a visual representation of the data but also to enable users to use intuitive tools for data analysis and visualization is growing. According to Gartner, in the past years, the best tool for such analysis is Power BI, a complex tool that combines Power Query, Visualization, and very powerful algorithms for complex analysis of big datasets.


Power BI can be used by organizations in two ways. First, it can be used as a tool for Power BI experts who have the knowledge and skills to manipulate, transform, and prepare data for visualization. Second, Power BI can be used as a front-end for a powerful, flexible, and intuitive analysis layer that sits on top of all databases and data warehouses. This analysis layer allows users to easily access and analyze data without having the skills of a Power BI expert.


The expertise that Data Masters provides in the implementation of the solution takes into account the needs of the departments within the organization. Understanding the business needs from one side, while being technically prepared with knowledge gathered from multiple years of experience, helps us be reliable consulting partners that guide you in translating your needs into a proper model. 


The Data Masters Methodology 


As a consulting company, we deliver the best practices within one organization, as a goal that is based on our general value proposition. Our methodology of work combines:


consulting – tailoring the best possible solution for the client;

education a combination of workshops and sessions with the business users that provides both acceptance, early feedback, and a possibility for them to learn as much as possible about the solution;

implementation – the final piece of the puzzle, where our consultants deliver the best practice on the market, in terms of solution design, architecture, and processes. 


Our methodology helps businesses address the biggest challenges, such as redundant reports, data-driven business decision-making and processes supported by Machine Learning, and taking the first steps towards Prescriptive modeling. This approach also allows to address challenges such as lack of documentation, lack of opportunity for fast ad-hoc analysis, and slow generation of reports for a long time interval, and also solves the problem that emerges when there are too many spreadsheets, but no visualizations. The single source of truth and the predefined filters are the final benefits of the solution.


Why Data Masters? 


Data Masters is a consultancy company focused exclusively on the field of Data Science with a very skilled and strong team, ready to deliver and advise on any kind of data-specialized and business-oriented issues together with the business partner. With the experience we have in the area, combined with wide domain knowledge, we can deliver Self-Service Analytics to any sector, combining different experiences in the area of Business Intelligence and Advanced Analytics. 


One of our greatest differentiators is the international experience of most of our consultants who are focused on creating a business approach that delivers results step by step among a consensus roadmap, as well as mentorship and building “Data Masters” within the organizations that act as a bridge between the business users and analytics.