Part 2: Kill the Time Vampires, Empower the Data Analysts
Time is of the essence in business decisions. If analytics takes longer, decisions come in slower. These lags end up costing businesses opportunities and profits. At the pace that things are moving in today’s world, agile analytics is the need of the hour and empowering data analysts can help achieve this agility.
The first part of this blog-set – “Time Vampires Holding Data Analysts Back”, discussed the key challenges faced by data analysts. In this concluding part, we offer resolutions to said challenges so as to truly empower the data analyst. And this is where the era of next-gen analytics comes into the picture, essentially through a self-service platform.
The Self-Service Analytics Platform
Imagine the immense potential of a world where you can avail any expertise that you need just with the click of a button and with no additional costs – it would alter the way we thought. That is where we want to reach in the analytics world.
Data scientists are highly skilled experts who design statistical models to gain business insights. These incredibly complex models cannot be learned and mastered by everyone, but every business function today requires them. So far, these experts have had to design, implement, and deliver results from such models. The process is expensive and time-consuming, not to mention the limitations of depending on a single premium resource. Analytics needs to reach a stage where experts only design statistical models, while the rest of the process is taken care of by analysts who are empowered with an able self-service analytics platform.
So, what is it that would make for a truly integrated and complete platform which empowers data analysts with an accurate and quick turnaround? The main functions that would go into this solution and resolve most of the issues would be – data preparation and accessibility, basic and advanced analytics, and finally visualization and collaboration.
Here’s a look at each of these.
Built-in Data Preparation and Accessibility:
As simple as this may sound, these functions are extremely integral elements of the analytics spectrum. Through such built-in support, analysts can enjoy the freedom to pick data as and when they like because the entire data dump is at their disposal, without any IT dependency.
Irrespective of disparate formats and sources, they can easily use the data preparation functionality to suit the processing model they employ. All issues related to accuracy, disparity, and duplication of data are thus automatically taken care of. Instead of expending 90% of their time on cleaning and formatting the data, analysts can now utilize it for actual analysis.
Support for Basic and Advanced Analytics:
The ability to leverage statistical models with a short turnaround time, including advanced analytics for predictive models, is a necessity today. This can be achieved in integrated tools with the same process flow as a simple analytics model. With features such as drag-and-drop, inbuilt algorithms and so on, analysts can come close to scientist-level expertise. In addition to this, having machine learning as an embedded capability means that not only can one intrinsically reuse all previous logic, but the tool will also become more and more intelligent with time and reduce your time-to-results drastically. Such a result-driven approach is much easier to justify in terms of ROI as well.
Integrated Visualization and Collaboration:
An integral element of this comprehensive model, visualization is also the key to communication. In fact, the ultimate aim of preparation, modeling, and processing of the data is to get to an apt visual that enables decision making. The more holistic the charts and graphs, the more intuitive the insight. With visualization integrated, data will no longer have to be taken out of one system and put into another; neither will there be a dependency on a third-party license to get the best views that one wants.
Add collaboration abilities to this mix and all problems related to complexity will also get addressed. Enabling collaboration ensures that within the same tool, input can be sought from relevant parties and unified output and insights can be attained. This multi-party involvement, in turn, tackles concerns of emotional attachment to hypothesis and tendencies to extrapolate data.
If we truly want to achieve total empowerment of the data analyst, at the same time reduce the cost of doing analytics, and still guarantee quick and actionable insights, the aspects outlined above have to come under one unit – the self-service analytics platform. With such an integrated end-to-end solution, security is also intrinsically ensured, since data no longer has to move in and out of multiple tools.
Accelerite’s ShareInsights delivers next-gen self-service analytics in its most complete form. Not only does it cover all the aspects outlined above, it also manages factors such as deployment time and incorporation of all your data sources. Additionally, its Insight2Action feature automates the association of actions to insights, removing the need for human intervention in responding to business needs. Learn more about ShareInsights here.