We currently live in an era where data is the oil that drives businesses of all sizes. Having data insights gives organizations a competitive edge and helps make well-informed decisions. Businesses today aggregate large sums of data involving customer profiles, sales and marketing statistics, financial metrics etc., in order to derive business insights from it. Capturing and analyzing torrential amounts of data is only the first step towards leveraging data in the organization. To truly make data work in your organization and build a culture of analytics, businesses should make the data easy to access and consume for decision-makers.
Before learning how chatbots can help create a culture of analytics, let’s understand why adopting such culture is important.
In order to create significant value out of data and analytics, corporations need a shared mentality across the company that recognizes the importance of data in business. It is important that collectively as a company, everyone learns to map business growth and success to decisions made keeping the analytical insights in mind. A culture of analytics hence becomes crucial in triggering well-informed decision-making processes. When the ‘culture of analytics’ is established in a company, using data and insights while making business decisions becomes second-nature to the employees improving overall efficiency in the organization.
Historically too, it has been observed that companies that use reliable data-driven analytics as a culture, see a steady rise in business growth. Their productivity and profitability rates are about 5 to 6 percent higher than their peers that fail to do so. According to a study by McKinsey, organizations see a six percent increase in profits in the first year when using data-driven analytics, which grows to nine percent after five years. According to the Harvard Business Review, data-driven decision-making practices are on the rise. The global big data market which is currently evaluated at $1.7 billion is predicted to swell to $9.4 billion by 2020.
For most organizations trying to implement a culture of analytics, the biggest roadblock is employee adoption of business intelligence and analytics tools. According to Gartner, the BI adoption among the workforce is just over 30 percent. A lack of adoption from the employees means that any possibility of gaining value from the data generated is reduced. Let’s understand some common reasons for such low adoption rates of BI.
A prime reason employees are not willing to embrace BI and data analytics solutions, is that most of these tools are quite difficult to use. Not having an interface that provides seamless user experience (UX) can lead to employees avoiding such tools altogether, hampering the company’s culture of analytics. BI and analytics tools that require all users to be tech-savvy, limit user adoption. Business users again have to depend on IT or MIS teams to generate reports or access insights.
Most analytical tools require the user to go through a long list of steps in order to access even the simplest bits of information. For businesses dependent on data insights to make everyday decisions, it is imperative that data is available on the touch of a button. However, decision-makers today have to log in to the BI system, navigate through different windows, filter dashboards and generate reports for data accession and discovery. This tedious process doesn’t fit well in the daily workflow of business users and they may not be motivated to undergo all these steps to get the data they need.
Picture this as an example; you are brainstorming with the marketing team and need statistics on how your past three digital campaigns have fared. You wish to factor in national holidays and weather conditions at the time. But if the tool requires you to sign in to the BI systems, filter dashboards, download year-wise reports and then run these reports to collect the insights. You may tend to do this once or twice. But on a daily basis and when the time is crunch, you would just want to skip all these steps altogether and take decisions based on gut.
These are some of the factors because of which employees usually find analytical tools difficult to use and prefer to make business decisions without their aid, hampering the company’s ‘culture of analytics’.
A chatbot is a computer program or an artificial intelligence system that conducts a conversation via auditory or textual methods. Chatbots are often designed to convincingly simulate how a human would behave as a conversational partner and are currently used various practical use cases in the areas of customer service, ITSM, HR etc.
With respect to Business Intelligence, chatbots provide data-driven insights to decision-makers, enabling them to make crucial business decisions quickly. By integrating chatbots into your existing BI systems like PowerBI, Oracle, SAP, etc., you can enable business users to access data from their actively used messaging app like Skype, Skype For Business, Slack, etc., via chat. A Business Intelligence chatbot resides as a contact in a messaging app and users can have natural language conversations like “what is the total marketing spend in 2016” to access data. Chatbots essentially become the single point of contact for employees to access business insights quickly. BI chatbots today are developed to work on multi-device interfaces, making it convenient for business users to access information on the go.
They also have the capability to display reports and data in multi-media formats like a pie chart, bar graph or even simple text. BI chatbot can also back the sent information with a link to the relevant BI dashboard if the business user wants further in-depth insights.
BI and analytics chatbots simplify data consumption and interaction for business users. Decision-makers no longer have to undergo the hassle of filtering dashboards or logging in to the BI system to fetch data. You can be in the middle of a meeting and simply ask the chatbot to send the required business insight. This convenience of accessing data via chat motivates decision-makers and employees to use data more often while taking decisions enabling a culture of analytics in an organization.