With digitization underway at organizations of all sizes across industries, data is establishing itself as the most critical factor for ensuring success.
NB: This is an article from RateGain
Companies use data to make informed decisions about product design, marketing and distribution strategy, customer experience, manufacturing operations, and more. Accelerating the process of converting data to insights to decisions is critical to achieving a competitive edge and consistent business growth. But, what is stopping enterprises from making the best use of that omnipresent data? Data explosion – there is too much data to be collected, analyzed and acted upon.
Data is continuously being generated from business operations, customer interactions, financial transactions, and more, making it difficult for business executives to derive actionable insights from it at the same pace. According to some studies the total amount of data created across the globe will cross 175 zetabytes in 2025. In such a scenario, how can businesses extract value from data? While there is a plethora of analytics tools and platforms available in the market that churn the data and promote informed decision making, we will be discussing a key aspect of data intelligence – data visualization. At those high data volumes, data visualization becomes a mammoth task and needs to be augmented using conversational UI with artificial intelligence and machine learning working in the background.
Picturing the one-zeros
Data visualization is not new to us. Humans have been finding ways to understand data with graphical representations that help consumers of that data to draw meaning from numbers, interpret data trends and bolster decision making. In recent years, business executives have relied heavily on Excel to collate and analyze data and share insights. However, with the volume, variety and velocity of data growing manifold, it has become increasingly impossible to scale up data intelligence processes with Excel. Newer tools and platforms such as Tableau have emerged to tackle the unique challenges of Big Data. Business intelligence (BI) as a domain now has many enthusiasts – both organizations trying to derive value from data and organizations building leading-edge BI solutions. Business intelligence has been proven useful as it makes data more rich and insightful, simplifying decision making on complex problems.
In the travel and hospitality space, in-depth data insights into customer behavior, operational performance and market dynamics can have a significant impact on revenue growth and profitability. And gaining that intelligence from data would require companies relying on traditional data tools and technologies to overhaul their existing analytics landscape, making way for a faster data to insights lifecycle. In a fast-paced business environment where organizational efficiency is key, spending considerable time and effort on reading and interpreting long Excel sheets seems wasteful and unnecessary. Business users in the travel and hospitality space are looking to identify trends quickly and take appropriate actions in real time. They want a single, holistic view of their data which may be spread across geographies, disparate systems and processes, and departments.
Unearthing the meaning in data
Depending on who is consuming the data, their data visualization needs may differ. Let’s consider three user profiles:
- Controller: Requires an easy to understand, intuitive view of data to make decisions quickly and accurately
- Digger: Enjoys playing with data and trying permutations and combinations to find hidden trends and correlations.
- Doer: Executes business processes and may need to make simple ‘go/no-go’ decisions by looking at data.
That brings the idea of ‘adaptable data visualization’ to the forefront. Not all users would like to see data insights presented in the same manner – some would like it to be more granular and rich, while others may prefer a 10,000-feet view of their data. The idea is to get rid of elements that might not pique the interest of the user or may be irrelevant to them.
By providing easy to consume insights upfront and empowering the user to construct granular views and manipulate data based on their business requirements, companies can gain the trust of the ‘controller’ and ‘digger’ personas. That yields itself well to organizations looking to build a robust data-centric culture where “every single person — regardless of expertise or tenure – should be enabled to make better decisions based on data.”
How to drive BI excellence for your customer
Define BI objectives – At the outset, it is important outline what level of intelligence you are looking to offer with your BI implementation. Define the inputs and outputs and determine how you will process the data.
Take a funnel approach – Companies must build a funnel view for effective data consumption – know what is required on the dashboard and allow users to dig deeper depending on the insights they need. Funnel views offer the flexibility and superior user experience that is necessary for promoting data centricity across the organization. Moreover, choosing the right graph type to keep the visualization simple and interactive can help in boosting engagement and presenting data in a more consumable manner.
Don’t contain everything in one dashboard – The dashboard is to be used as a health meter, where everyday someone logs in to see what’s working and what’s broken. Cluttering it with too much information will defeat the purpose for which it was built – delivering insights that are easy to understand.
Choose colors carefully – Interestingly, colors also play a significant role in ensuring effectiveness of data intelligence. Colors are not just used for aesthetics, but they convey meaning, drawing your attention towards critical information. A common example is that of using ‘red’ and ‘green’ while indicating losses and gains. Those colors alert the users to investigate further and contain the losses. According to some experts, color in data visualization can also be used a tool to invoke emotion and set the tone for the audience.
Deploy the right BI engine – Selecting a robust, scalable and future-ready BI engine that reduces time to insights is necessary to scale up your BI program successfully. A BI engine that can quickly ingest disparate data, filter out data based on business requirements, all the while ensuring data consistency and integrity, may prove to be your best bet. While making the choice, you must consider cost efficiency, scalability, and interoperability of the solution. Discussing all the ‘what-if’ scenarios may be important to the decision-making process, however, testing the engine with actual business users will ensure the choice you make delivers the expected returns.
Conclusion
From my extensive experience of working with global organizations looking to accelerate time to value associated with data and analytics projects, I have realized the important role that data visualization plays in ensuring success. With the learnings I have had over the years by looking at how users consume data, I firmly believe refining your data visualization practices is paramount if delivering value to the end user in the form of actionable insights is your primary objective. The next time you see a chart, table or page in a data analysis report which seems unnecessary or fragmented, go ahead and get rid of it. Keeping it simple and intuitive is what works – every single time.
