Big Data adds speed and accuracy to decision making: Tableau

In a conversation on the sidelines of analytics and big data, Deepak Ghodke, country manager, India, Tableau divulged Dataquest how big data is helping enterprises to transform and take their business to the next level.

How is big data changing the way businesses work?

Previously data was available but it was very little and needed a significant amount of effort to derive insights from this data. This is why businesses relied on opinions which were derived from experience to make decisions. As data technologies progressed, data was being collected but the frequency was very low and new data for newer insights was still very difficult and hence businesses continued to rely heavily on opinions.

Today in the age of big data we have access to volumes of data, coming at high velocity from variety of data sources. This combined with the ability to visualize this information by end users makes decision making easy and insightful. For examples Tableau users at organizations like Ashok Leyland had to wait for weeks to see their reports and insights. Today in contrast they can see information daily with the ability to answer additional questions on their own in a matter of minutes. Big Data has caused changes in speed and accuracy of decision making.

How can businesses of all sizes make use of analytics?

As businesses become increasingly data driven, the need to derive insights from huge chunks of data continues to increase, leading to the demand for self-service big data analytics.

Every line of business can be optimised by implementing insights derived by big data analytics tools. By using technologies such as predictive analysis, trend monitoring, real-time data visualisations and dashboards, big data converts each and every action of a customer or business function into quantifiable insights. These insights may include consumer behaviour, sales effectiveness, revenue management, supply chain management, marketing campaign efficiency etc that will help empower businesses to make insights-driven decisions.

Even in areas like social media, Tableau for instance has been able to help customers understand the impact of their social media campaigns by delivering insights on the performance of every post, segment data by demographics and geography and identifying loyal advocates.

The applications of big data analytical tools thus can be across verticals and almost all industries may benefit by the right selection of technology. Some of Tableau’s customers like Eveready have been able to achieve an ROI of over 500% by deploying Tableau and gaining valuable insights that helped the company significantly boost sales and improve efficiency in supply chain management. Using Tableau, Eveready has been able to drastically speed up decision making, achieve growth in sales volume and mine existing data for valuable business insights.

Does big data eliminate the need for data warehousing?

Organisations can of course continue to learn a lot about the business and their customers from BI programs and data warehouses. However, big data analytics is where advanced analytic techniques operate on big data sets—one of the most profound trends in business intelligence today.

Big data analytics is the intersection of two technical entities that have come together. First, there’s big data for massive amounts of detailed information. Second, there’s advanced analytics, which can include predictive analytics, data mining, statistics, artificial intelligence, natural language processing, and so on. Put them together and you get big data analytics, the hottest new practice in BI.

Using advanced analytics, businesses can study big data to understand the current state of the business and track still-evolving aspects such as customer behavior. This empowers users to explore granular details of business operations and customer interactions that seldom find their way into a data warehouse or standard report.

Some organisations are already managing big data in their enterprise data warehouses (EDWs), while others have designed their DWs for the well-understood, auditable, and squeaky clean data that the average business report demands. The former tend to manage big data in the EDW and execute most analytic processing there, whereas the latter tend to distribute their efforts onto secondary analytic platforms. There are also hybrid approaches.

Regardless of approach, user organizations are currently reevaluating their data strategies. In response to the demand for platforms suited to big data analytics, data vendors have released a slew of new product types including analytic databases, data warehouse appliances, columnar databases, no-SQL databases, distributed file systems, and so on. There is also a new slew of analytic tools.

In effect the need for data warehousing will remain as the need to collect, store and prepare the data would be necessary. The change is happening, in the way this task is accomplished by either enabling this in various types of technologies or by holding the data in various types of locations as well.

How do you see the role of self-service analytics in today's enterprises?

The new thing in the two decade old domain of Business intelligence is self-service analytics. This approach will enable all users to answer their own question and this will continue to be one of the fastest-moving areas in the enterprise. Since the techniques people use to drive adoption and get value from their data are multiplying, it is also leading to an increased demand for self-service analytics. This has been witnessed amongst many major companies using big data. Companies have started to prefer tools that can be used in-house rather than hiring an external agency to carry out the job for them. These in-house tools are meant to be used by end users in a self-service mode. This increase in users has led Gartner to estimate that the business intelligence market will reach $213.8 million in 2017 which is an 18.6% increase over the 2015 spending.

India is currently among top 10 big data analytics markets in the world. By 2025, the big data analytics sector in India is expected to grow eight folds to $16 billion accordingly to a recent report by NASSCOM.

In fact Linkedin has named statistical analysis and data mining as the second hottest skill that can get an individual hired in 2016 globally. It is the only skill that has been consistently ranked top 4 in 10 countries analysed, suggesting that businesses are still hiring experts aggressively in data storage, retrieval and analysis.

At Tableau, our mission is to help people see and understand their data. In fact, we have recently launched Tableau 10 to reflect our commitment in making it easier and faster for people to work with data. One of the most important themes of Tableau 10 is to ensure that self-service analytics can be furthered for all kinds of users. Hence, this updated version has a fresh new design which makes it easy for users to grasp the insights from their data. This also includes new analytical and mobile enhancements, options for preparing data and a host of new enterprise capabilities.

What are the challenges faced by the companies to adopt Big Data Analytics?

Big Data has been one of the game changing technologies of our time and we are witnessing a continuous increase in its adoption across industries. Within in the overarching field, data visualisation and self-service analytics have been gaining momentum. This will naturally lead to a challenge of choices. Which product to choose, in which formats and how to deploy these products. The simple approach that we may suggest is to try these products in the customers own environment to actually understand their efficacy.

Organisations will have to stay open to the possibilities of new products that can directly connect to a wide variety of formats, as long as they can deliver enough to justify bringing them into their existing environment. Also, part of the new IT environment will see Hadoop and data warehouse solutions co-exist. The variety of data sources, their storage, the cleansing of this information and then assimilating these together will be a key challenge that organizations will have to face and solve.

Big data will be brought down to eye level when one visualises it. People working with data will be able to change the data they are looking at to answer different questions and change the way they look at data as well because each visualisation or dashboard may answer different cycles. Thus, users are empowered for big insights. The user in this case has to be knowledgeable to understand how to see this visualized data. For Tableau users we have made this simple through our “Show Me” algorithms which pack a significant amount of techniques which automatically show the best visualizations for the dataset that the user is using.

Data of different sizes, formats and types will need to be blended together to understand the business and its customers, and allow you to ask and answer questions as they come to mind.

Data integrity and adopting governance will also be key in big data management.

In the future, newer technologies like predictive and cognitive analytics, artificial intelligence and deep learning algorithms may gain precedence. With the increase in IoT adoption, there will be even larger amounts of data being generated real time and there will be better integration of various solutions to help us improve various aspects of our lives.