By Ankur Goel
Imagine for a moment that you are the president of a country which has recently discovered that it has a massive amount of gold reserve just waiting to be utilised. This gold, if harnessed effectively, could help you in revitalising the country’s economy. What’s more, it could help you attract more international trade and drive technological innovation. Sounds good, doesn’t it?
There’s just one problem. Your gold reserve is distributed unequally across multiple geographies and topographies, some of which are quite inaccessible. Gaining meaningful value from this reserve, as a result, is extremely challenging. You are barely able to extract enough gold within a given period of time to fulfil the requirements of your own economy, and have little to nothing leftover to power all that international trade you were hoping would help put your nation on the world map.
Sounds like too far-fetched a scenario? Replace “president” with “CXO”, “country” with “company”, and “gold” with “data”, and the aforementioned hypothetical situation is quite indicative of the real-life challenge that modern-day businesses are facing today – that of deriving the maximum value from their massive information reserves.
The challenge of implementing analytics
The world is generating information at a much higher pace today than at any other time in its history. From sales, marketing, and customer support to internal compliance, finance, productivity, and employee satisfaction, data can help make every function more efficient, effective, and valuable. Doing so, however, requires that data to be harnessed effectively.
Most CIOs or CDOs who start analytics projects find this aspect the most challenging, especially when using traditional tools. This, in turn, has given rise to certain notions about BI and analytics that no longer hold true. Here are 4 common myths about using analytics within the organisation which new-age solutions are busting with their innovative approach to data:
- It’s not possible to bring together all your data
Data is stored across disparate data environments today due to various regulatory, security, and compliance requirements. Traditional BI and analytics tools find this hurdle difficult to navigate, as they require the siloed data to be processed and prepared before it can be analysed. The delay that this approach causes erodes the value of analytics; accessibility and availability of information becomes a major challenge, plus there is always a likelihood of critical data being missed. As a result, most executives feel that it is almost impossible to bring together all of business data together in one place.
New-age platforms like Qlik have the ability to swiftly collate and combine information from across multiple data environments – on-premise, hybrid, or cloud – within a very short span. This data is then embedded within the end-user workflows for better accessibility and availability, at the time and place of need. Such an on-demand BI approach strengthens the decision-making process across the board by helping business users have a more complete view of their data. The secret to making good decisions after all is having context, and combining more data allows for meaningful, agile, and value-centric actions to be taken.
- You need to prepare every question business users will ask the data
Old-school relational databases and SQL queries were not designed for modern analytics. This is a major flaw in most visualisation software and results in restricted linear exploration and analysis on partial subsets of data. With data experts having to make assumptions about what kind of queries business users will put forth, data is often left behind. Modern analytics does away with this need by leveraging an associative approach which finds data associations between multiple information sets. This equips businesses with the capability to not just understand and answer the ‘what’ of business intelligence, but also swiftly explore the ‘why’ and ‘what next’. Making data more accessible and available at all times also significantly brings down the time taken to generate insights and transition from analysis to application. It additionally enables users to realise business value that might have otherwise remained hidden, giving organisations a much-needed competitive edge within the market.
- You need to employ an army of data scientists
It is a common belief that analytics tools can only be operated by professionals specialising in data sciences and analytics. This is why most businesses end up setting up data teams to handle their BI queries, inadvertently creating a data analytics funnel and limiting the number of business users who can use analytics to extract insights from the available data. The final results are made available anywhere within a couple of hours to several weeks, depending on the priority of the request and the volume of data that needs to be sifted through.
Self-serve analytics, on the other hand, presents the relevant information within the workflow through an interactive and easy-to-use click-based visual interface. This simplifies the complexity usually associated with conventional BI and analytics tools and allows even non-technical business users to self-service their own queries. Businesses can also unlock significant savings on the costs associated with creating and managing an internal data team.
- Letting business users self-serve their data is not safe
Governance should not be an afterthought, especially when it comes to data, but traditional BI tools are not equipped for flexible data governance. Modern data analytics platforms, on the other hand, are inherently designed to facilitate better security, while enabling collaboration. This not only helps in enhancing productivity and minimising costs, but also in identifying operational/data bottlenecks and optimising business processes. The holistic, end-to-end impact that such BI solutions deliver within a secure and governed data framework is why leading organisations across the globe are now deploying them across multiple business functions.
Platform-agnostic, associative data analytics: A new-age BI solution for new-age business challenges
The hurdles mentioned above are being addressed through innovative, cutting-edge BI and data analytics platforms like Qlik. These platforms are designed specifically to extract the business data out of the various silos, process it, analyse it, and present it in the form of an interactive, easy-to-use visualisation. This also helps in elevating business intelligence from being a largely-reactive application to a proactive application which can influence and create meaningful opportunities for businesses.
The analogy that we began with is not completely untrue; data has well and truly emerged as the new gold for the global business landscape. How much value businesses are able to extract from it, ultimately, depends on how effectively they are able manage the information that they have available to them. There are ways to realise the maximum value from the unique, abundant resource called data. In a day and age powered by technology, deploying analytics is not only preferable, but increasingly imperative.
(The author is the Managing Director, Qlik India)
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