All businesses that function in today’s economy have one overarching requirement which is the consolidation and management of monumental volumes of data. It is not just the handling of such data that is important but also the ability to utilize it from a data-driven standpoint. The survival of an enterprise is dependent on insights drawn from data collation that can be put to use while making decisions designed to drive value in the future.

A data-centric approach is influenced by three factors – the availability of data, access to that data, and further access to insight from the data. The development of these attributes over the course of time has now brought us to the age of data democratization. The Harvard Business Review attests that over 95% of industry leaders state that commercialized access to data and analytics throughout the enterprise is crucial to business accomplishments.

How the Domain of Data Evolved to Give Us Big Data

It all began with the introduction of Enterprise Data Warehouses (EDWs) as a solution to entrepreneurial operations reporting in the early 90s. People began to extract additional services from EDWs that were not limited to the simple storage of information but instead could answer questions along the lines of – how much stock is left in inventory post all accumulated sales? Or what trend does the comparison between sales for the first fortnight against the second-fortnight show?

With the advent of such a line of questioning came a paradigm shift in the way people handled Business Intelligence (BI). Newly designed tools and skills were cropping up everywhere in order to cater to this shift in the nature of drawing insights as it moved to a more self-service model. As the digitization of all habits including commercial, communicative, and entertainment-based was now commercially available, it opened new pathways for businesses to interpret and thereby understand their client much better.

New big data tools provided data engineering experts access to this data who stored it and also made it available to expert users who could extract insights. Enterprises also laid out preliminary analytics data lakes expected to generate significant value due to the recent rise of self-service BI. However, the systems were too complicated for even skilled users who were unable to glean any insights. As it turned out simply broadening access to data and insights would not be enough to have an impact on business optimization.

Access to Insights with Extensively Simplified Tools: Modern Data Platform Cloud

Modern data platforms concentrated on helping users help themselves. This meant working with what they had instead of attempting to instill a new skill set. By automating their backend technology stack, they could now provide “serverless” analytics which meant technical tasks associated with managing the stack could be overseen without user input. Modern data platforms then created easy to comprehend user tools that could allow them to focus on their work while machines would be working behind the scenes to handle the complexities of implementing the input.

Enabling Data Analysts to Create Meaningful Insight from Data Access

The ideal candidate to generate insights is someone with holistic knowledge regarding data management as well as the challenges faced by the business. In most companies, this person will be a data analyst. By decoupling both compute and storage operations, modern platforms and data management tools have unlocked new capabilities for data analysts revealing a whole new window into their business.

Since the separation of computing and storage allowed such tools to save information more efficiently as compared to other EDWs. Customers could now use a “structured data lake” approach to warehousing while increasing the prevalence of ELT (Extract-Load-Transform) with the help of SQL.

Enabling Users to Generate Self-service Insights with Intuitive Tools

The usual method in garnering insights is to send in a request and wait for a response. This pattern can be broken when automated systems are brought into the mix. Since they can be built with easily understandable functions users can extrude their insights without any hurdles in understanding functions.

BI tools are amongst the most frequently used portals for when users would like to gather their own insights or make decisions based on the ones already available. The limitation here is that insights are only as credible as the server that is behind them. The serverless backend that can be found with modernized data storage and management tools works with a number of widespread BI tools making self-service BI easier than ever before.

This already simplified approach to self-service BI is being deconstructed even more with user-friendly and self-serviceable query methods. This could be through using the familiar face of a spreadsheet interface. Apart from spreadsheets user abilities to access democratized data have expanded into Natural Language Processing (NLP). With a Data question-and-answer format, users who are not well-versed in data competencies and technicalities can also leverage insights by asking a question in simple natural language. In this way, insights can be extracted in a conversational manner. The opportunities for literally anybody to access data insights and generate them on their own have just grown exponentially thanks to progress in user inclusivity.  This maximizes the value of data and insights from it for a broad spectrum of enterprise users.

Are you interested in implementing democratized data to help your business drive value? Our team at DataSwitch would be happy to help. DataSwitch provides Intuitive, Predictive, and Self-Serviceable Schema redesign from 3NF to Document Model, as well as fully automated data migration & transformation based on the redesigned schema and no-touch code conversion from legacy data scripts to modern, MongoDB APIs.

You can count on DataSwitch for cost-effective, accelerated solutions for digital data transformation and modernization through MongoDB. Our no-code and low code solutions along with enhanced automation, cloud data expertise, and unique schema generation accelerate time to market. Get your enterprise’s cloud-driven data modernization journey running at light speed with business continuity ensured. Book a demo to know more.