Every organization is moving towards a data-driven culture. Companies of all sizes have woken up to the fact that they can now collect store and analyze data fairly easily and economically. That apart, the huge volume of petabytes of data has become a strategic asset now. Earlier the ease of data management and analytics was absent, but with the advent of open source technologies that can manage, cleanse, and analyze data, companies are wanting to unearth the data treasure trove.
Various stakeholders in the organization have different expectations and requirements from the data. In this blog, let’s take a look at various such data consumers and what they expect from their data and data tools.
Let us take a top-down approach and start with the decision-makers who are right there at the top of the food chain. They majorly don’t have much time and need the data to be presented in a visual format to them. Their primary objective is to leverage data to improve productivity and gain significant competitive advantage by growing their business. There are many tools available in the market where data visualizer can feed the data, chose the algorithm, and get the data represented in a visual. Such a visual representation of data makes it extremely easy for executives to consume and take action. These business owners are subject matter experts (fondly called as Citizen Data Scientists) and not necessarily technology experts.
They need access to easy-to-use tools that will allow them to slice and dice the data and get real-time insights to take quick business decisions.
Next in the hierarchy are the data scientists. These are the people on the ground who validate the collected attributes, the quality and run the analytical tools on the data sets to come up with inferences and insights. Data scientists need to understand the business goals and provide predictive analytics to come up with consumable forecasts based on which organizations can take relevant actions and decisions. They will have to crunch the numbers and keep it aligned with the ask of the business.
While data scientists also well-versed with various technologies and tools, they need access to the right tools that can help them quickly build models and derive real-time insights.
Analytics Leaders are the champions who have the end to end knowledge of how the data science team and department should operate, and they provide course corrections wherever necessary. They have to look at the value that needs to be discovered and set the correct priorities.
They need access to toolsets that can help them in the end to end extraction, modeling, and selection of algorithms for data analysis and visualizations. This makes it easier for the business folks as well as the analytics managers to define the priorities for data science initiatives based on business needs.
Once the data has been analyzed, it needs to be presented to the business stakeholders to help them take timely business decisions. The presentation needs to happen in the form of visual graphs and charts that can be easily consumed by the executives.
They need access to efficient tools that can allow them to convert huge volumes of data into attractive, dynamic, efficient, and meaningful presentations.
Statisticians are another important key stakeholder in organizations. Their importance is pretty much self-explanatory. They bring the statistical knowledge which is required for fitting in the models and running the analysis. The other key stakeholders are the analysts, who know which data to be collated and scrubbed to be made analytics-ready. This is a complicated task as different levels of queries need to be performed on various internal and external sources.
Many organizations are now leveraging the power of artificial intelligence and machine learning to implement chatbots and recommendation engines. In that scenario, they take the help of AL/ML specialists. These specialists understand the nuances of AI/ML like REST APIs, SQL, etc.. They can also implement standard machine learning algorithms such as clustering, classification, and perform A/B testing, and build data pipelines.
They need access to tools that will allow them to get the right data and use it appropriately in their models to refine those.
Many organizations use data lakes on cloud and various enterprise-level data warehouses to support their analytics initiatives. Data Engineers are responsible for building enormous reservoirs for big data. They construct, develop, test, and also maintain architectures such as large-scale data processing systems to boost the performance of the databases.
They need tools to help them easily learn and adapt to modeling techniques and build solutions.
Considering the varying needs of different stakeholders, organizations often end up investing in multiple tools. It creates huge complexity in the IT stack which is difficult to maintain and is extremely costly. Organizations also need to then spend time and energy in training the various stakeholders on different tools and ensure that the different tools integrate and facilitate smooth data exchange without any issues.
Rubiscape, a disruptive data science platform, aims to a provide solution to all these problems. This easy-to-use platform can be used by each one of the above-mentioned stakeholders to address their specific needs.