NOUS INFOSYSTEMS LEVERAGING INTELLECT BI TRENDS. More Power to Users, Better Insights. Karthik Valluri BI lead

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1 NOUS INFOSYSTEMS LEVERAGING INTELLECT TM R BI TRENDS More Power to Users, Better Insights Karthik Valluri BI lead

2 BI EVOLUTION The BI Ecosystem has been dramatically changed over the past few years. At the bottom of this technology stack, we had data aggregated from different source systems through a complex ETL structure culminating into one data warehouse. We, then had a semantic layer where most of the reporting needs were met using traditional Reporting tools. On top of this layer, we had the advanced analytics stack which was used to create dashboards and KPI s. A high dependency on IT to create reports and dashboards for business has been a disadvantage of the above model. With newer tools, this process has become faster but still tends to lag behind business demand. It has evolved over time and now we have three prominent players like Qlik, Power BI and Tableau competing in this space, as per the latest Gartner s report. This transformation resulted in self-service BI where end users can create their own reports and dashboards, reducing dependency on IT. This empowers the business user in gaining insights on their own business data, without relying on any set of developers. In the post self-service era, Machine Learning algorithms developed from ML, are being used in these BI tools, making them more relevant and intuitive to work with. Along with this, we have microservices architecture where traditional BI systems have been fairly static. Going forward, vendors will be implementing concepts of microservices and Application Programming Interfaces (APIs), to embed their analytical content into websites or their internal applications. This expansion in BI Analytics is going to expand like never before, with advent of Artificial Intelligence and Machine Learning, making BI Analytics the most sought-after subject area. Here are some of the top BI trends that we have collated from both, our experience, as well as analysts like Gartner and Forrester. 2

3 SELF-SERVICE BI What is Self-service BI? In accordance with the Business Intelligence methodology, end users design and deploy their own reports and dashboards, within an approved and supported architecture. The goal is to make BI available to users anywhere within an organization, although IT maintains overall control over who has access to which tools and data, but IT no longer acts as a bottleneck to every query, dashboard/report requested. Traditional BI vs Self-service BI Traditional BI means, creating reports with reporting tools like crystal reports, excel, SSRS etc., which is time-consuming, highly dependent on IT and constant interaction with customers to ensure you get the logic right for every aspect of a report. In case of self-service, the logic is centralized in a single data-model and the end users get the flexibility of creating reports on the fly from it, depending on their requirements thus becoming faster, user-friendly and intuitive. Can they co-exist? Yes, typically, in case companies/enterprises have heavily invested in Data-warehouse, it will be tough to replace the existing investments. However, one can use self-service visualization tools on top of their data management layers to visualize and take quick management decisions. Also, enterprise reporting tools like SSRS, Crystal Reports, Cognos and MicroStrategy still rule the space of paginated reports that are essential for periodic standard reporting and compliance reporting, which is not effectively addressed by the self-service BI tools, even though tools like Qlik have NPrinting for paginated reports. Advantages Some of the advantages of Self-service BI tools are:- Visual Analytics Visual Analytics: - It is all about how you present the data, and how the end users are given the flexibility to slice/dice their data. Widgets and KPI s are some of the metric driven parameters that help end users make their decisions. Some of the tools in the market that ace this feature are Qlik Sense, Power BI, and Tableau. Scalability In-memory acceleration, an association between reports, ease of learning, and flexibility make self-service BI tools stand apart from others. On-demand, Predictability and Mobile Rendering One of the unique features, is the ability to create custom reports on the fly without taking much time and delivering the results to the end users via a phone or tablet. The dashboards seen on mobiles, tablets are extremely intuitive thereby giving great customer experience. Moreover, they are also equipped with predictive tools that are useful for data analytics. 3

4 EMBEDDED ANALYTICS What is Embedded Analytics? It is the art of integrating or using your analytical content, created from your self-service BI tools within your business applications. So, the end user does not have to jump from one application to another to gather data. For example, if embedded analytics is used correctly, a sales person using the internal CRM has access to the trends and metrics relevant to his or her customer directly embedded into the CRM. The core BI application will be used mostly by analysts to analyze and gain insights from data. These insights can be shared quite easily with the end users. One of the trends in software service development, is the microservices architecture. Traditional BI systems have been fairly monolithic; going forward, vendors will be implementing concepts of microservices and modern application programming interfaces (APIs), making it easier to put together personalized solutions and embed components of BI and analytics, such as dashboards and other visualizations, into other application services. Different Ways of Embedding So, how do we embed our dashboards/reports into our applications like websites or SharePoint? All the top tools like Qlik Sense, Power BI, and Tableau, provide the following methods:- IFRAME This is the most popular and easiest mode of deploying your dashboards/ reports to your applications. Basically, it is inserting an HTML document within another HTML document. So, one can use the URL s provided by the self-service BI tools, embed them in an iframe, and put them in a SharePoint or website thereby leveraging the analytical capability to its users. REST API The next best option which provides greater flexibility, is to use the REST API s, provided by the self-service BI tools. REST API s are nothing but Application Programming Interfaces that use HTTP/HTTPS to GET, POST, PUT, and DELETE data. However, this option requires developer to know some programming aspects of Javascript, HTML and CSS. 4

5 CLOUD BI Why Cloud BI? One of the traits of a smart business is to maximize profits while minimizing costs, that s where Cloud BI comes into play. Also called as SaaS or Software as a Service, all your key data driven decisions are kept in public or private clouds, thus providing a real-time and faster access to data. Some of the advantages of Cloud BI, compared to on-premise are:- Faster ROI With scalability and multi-tenancy, Cloud BI solutions automate everything from data discovery, data prediction, and ad-hoc reporting. The best part about this option is that all the infrastructure needs are handled by the cloud-hosting company and all you need to do is to focus on your data. Agility and Data Security All the Cloud BI solutions embrace agile technology, compared to the traditional waterfall model. With agile BI, the focus is on delivering pieces of BI functionality in manageable chunks via shorter development cycles rather than solving every BI problem at once. Agile methods promote shorter development cycles and delivery of iterations or releases that users can test and implement sooner. Security is another important factor, many deliver analytics through multi-tiered caching, data encryption, data segregation, security patches, and regulatory compliance. Deploy and Consume anywhere In today s world of mobile usage business, users want their metrics, dashboards up and running 24/7. This is where cloud solutions score over on-premise solutions. Mobile users can access reports, requirements, and make business decisions from mobile devices, tablets, and laptops elevating productivity from traditional to a new scale. 5

6 MACHINE LEARNING Why ML? Machine learning, coupled with embedded analytics, is a powerful combination that will drive down the manpower that an enterprise takes in. At the same time, provide extremely complex data analysis that a self-service tool fails to do by using complex algorithms. Gartner predicts that this ability, related to Natural language processing, is going to push companies make more insights on their data and make meaningful decisions that impact the very existence of business. So, machine learning can be defined as the practice of using algorithms to use data, learn from it and then predict trends. Machine learning does not replace the analysts but enhances their performance. The combined trends of BI and Data Analytics, and the increasing diversity of data, are moving natural language search functionality from nice-to-have features to must-have features for self-service tools. Search has had a kind of bolted-on presence in BI suites for a while now. However, users are demanding better integration of search functionality so that they can use it to find data quickly, write natural language queries, and perform operations by just asking questions rather than writing SQL. Artificial intelligence and cognitive technologies inside tools will help make search more intuitive. Can ML and Data Analytics co-exist? Data Analytics is very much needed to get insights on data so it is crucial for the developer to understand what is the data for and what it does, before writing algorithms for machines to automate. Some of the advantages of using ML are:- Data input from unlimited sources: - Massively scalable Voice & Image recognition:- Popular especially in mobiles Customization:- Creating personalized content based on user s preferences Sensory data Analysis: - It is widely sent to track patient s health and person s fitness in many gadgets Search Based Analytics One of the best use case of ML within a self-service BI tool, is the use of natural language processing to achieve search based BI results. Traditionally, we have been accustomed to get answers for questions asked in the form of reports or dashboards. However, this has dramatically changed with the advent of consumer information systems like Google, LinkedIn, and Yelp that have extensively used ML in every part of their solutions. In order to achieve this, we need to build a platform where we need to prepare data in such a way that we get answers for people instead of building answers in advance. This approach is adopted by major products like Amazon. As the questions asked by people change, the search suggestions change as well. Leveraging search-based BI is aiming to close that last mile for end user experience, and we believe that it is in the process of revolutionizing BI. One such tool that automates the process of search results by a single click is ThoughtSpot. It creates visualizations based on user searches, gives suggestions etc. They are one of the pioneers in this new trend of Business Intelligence. 6

7 CONCLUSION Self-service is the way forward to give that extra edge when it comes to decision making. This is very critical for management teams since they depend on underlying data. As suggested above, storing the data in cloud would provide a better return on investment and enable seamless connectivity without any down-times. With the advent of ML in today s market, self-service BI solutions provide extra power to the end user. The specific challenges of integrating ML capabilities in a self-service BI platform include, the supply of ML algorithms that do not rely on specific data and can be easily applied to customer specific use cases. Coming years should bring better tooling to support governance and management requirements, and improved alignment between popular development methods and the technologies. ABOUT AUTHOR Karthik Valluri is a BI lead at Nous, with a bachelor of engineering in electronics and communication. Karthik has been closely associated with implementation of several BI and Data Visualization projects involving, MSBI, PowerBI QlikView, Qlik Sense, and Tableau. REFERENCES

8 ABOUT NOUS Nous Infosystems is a CMMi Level 5 SVC + SSD v1.3, ISO 9001:2015 and ISO/IEC 27001:2013 certified global information technology firm providing software solutions and services across a broad spectrum of industries and domains. Nous Infosystems has been delivering quality technology outsourcing solutions to customers across varied industry domains for nearly two decades. Major offerings include Digital Transformation Solutions, Enterprise Mobility, Enterprise Social Collaboration, Usability Engineering, DevOps, CRM Solutions, Application Development & Maintenance, Business Intelligence /Analytics, Infrastructure Management Services, Product Engineering Services and Independent Testing Services. 20+ Years of Delivering World Class IT Solutions 700+ Product Releases 350+ Satisfied Customers Worldwide Skilled Workforce OUR FOCUS AREAS Digital Transformation Enterprise Mobility Big Data Business Intelligence DevOps Cloud Computing Usability Engineering Front-End Engineering Content Management & Portal CRM ServiceNow Automation & Performance Engineering CONTACT US New Jersey, USA Tel: California, USA Tel: Brentford, UK Tel: Toronto, Canada Tel: Mainz, Germany Tel: Bangalore, India Tel: Coimbatore, India Tel: Benefits of Upgrading to Dynamics info@nousinfo.com