The Growing Importance of Data
The global big data market is forecasted to grow to about 103 billion U.S. dollars by the year 2027 – Statista
It’s quite evident that we currently live in a time where there is a constant influx of information. Data continuously flows from a plethora of internal and external channels including computer systems, networks, social media, mobile phones etc., and is growing at a rapid pace. Leveraging data is no longer just a competitive advantage for businesses – it’s essential for survival. Companies are increasingly trying to harness these massive amounts of data to either reduce operational costs or boost revenue or both.
Limitations of Traditional Business Intelligence
The traditional Business Intelligence solutions that have been used over the past two decades have majorly been IT-driven and provided users with historical reporting. The underlying data architecture consisted of a centralized data storage solution- an Enterprise Data Warehouse (EDW).
EDWs are often connected to a large network of source systems and act as a central data repository. The data, before being used in reports and dashboards, is standardized, cleansed, and transformed in the EDW. While traditional BI solutions provided a lot of historical information, they are posing newer challenges.
Primarily, having to depend on IT specialists for data analysis is becoming a huge challenge for the business users.
Decision-makers today need access to accurate, real-time insights, and they need them swiftly – without depending and waiting for someone from the IT team to generate the report.
Other major challenges associated with traditional BI solutions include:
- Lack of On-Demand Analysis Capabilities – BI users do not prefer having to wait for analytical information. instead, they wish the BI solution to offer self-service capabilities in which they can analyze data sets themselves as per their own understanding and requirements, at any given time.
- Predictive and Prescriptive Analyses – Traditional BI solutions only provide insights into ‘what happened?’. However, companies today need a forward-thinking approach and wish to have insights into the future – ‘what will happen.’ and ‘what should we do.’ This is possible through a predictive and prescriptive analysis and modelling of data.
Unstructured data – Traditional BI platforms can harness only structured data. BI users today want to leverage data of various formats – semi-structured, unstructured, as well as third party data.
The Emergence of Modern Business Intelligence
Organizations today are trying to find better and more effective ways of turning business data into valuable insights. While you may find the numerous technologies, tools, and techniques involved in generating insights quite daunting, it need not be the case.
A modern BI analytics solution often doesn’t require you to completely overhaul your existing infrastructure. Instead, it complements your existing BI systems, making it easier for businesses to explore a varied range of insight-driven capabilities.
Business Intelligence is a broad function and modernizing it involves transformation across different stages – starting from data foundation and preparation to enablement, adoption and to ultimately creating a culture of analytics. It also requires a transformation also across all the involved elements, be it the people, process or technology. Here are a few essentials with which companies can get started from transitioning from a traditional BI solution to modern BI.
The Key Essentials of Modern Business Intelligence
1. Data Lake
An important step towards modern BI is to augment your EDW by integrating and adopting the concept of data lakes. Data lakes help a wider and individual business audience access and analyze data on demand. Unlike enterprise data warehouses (EDW) used in traditional BI systems, data lakes perform little to no automated cleansing of the data. This maintains data in its native form, sans filtration, allowing business users to efficiently access and analyze it and enabling efficient ingestion. A data lake used along with a data warehouse, offers a powerful foundation for modern data analysis and insight generation.
2. Augmented Analytics
In 2022, the modern BI market will exceed $6.25 billion, with growth increasingly fueled by augmented and search-based data discovery functionality – Gartner
Augmented Analytics helps relieve an organization’s dependence on data scientists or other manual processes by automating the insight generation process with the help of machine learning and artificial intelligence algorithms. With an augmented analytics system you can automatically go through your company’s data, clean it, analyze it, and convert data into actionable insights for decision-makers with little to no supervision from a technical expert.
It also reduces time-consuming exploration and weeds out false or less relevant insights automatically. In addition it make insight generation error-prone and highly accurate.
Learn more: Augmented Analytics – Everything You Need to Know
3. Business Intelligence Chatbots
With Business Intelligence chatbots, business users can access insights, reports and KPIs effortlessly via chat. BI chatbots can be integrated with any of your existing BI platforms like Power BI, SAP Business Objects, Tableau etc. Since a chatbot resides as a contact in their actively used messaging apps like Skype for Business, Slack, Skype etc., users can access data without switching between multiple windows, filtering dashboards or even logging into their BI system! This is especially beneficial for field sales personnel as they are required to access information to make decisions on the go. BI chatbots also help in increasing the overall BI adoption and enabling a culture of analytics!
Learn more: Chatbots For Business Intelligence
4. Self-Service BI
Self-service BI (SSBI) allows business users to access data as per their requirements without any dependence on IT and MIS teams. SSBI tools won’t require uses to have extensive knowledge about data analysis or BI.
Since SSBI puts a lot of power and responsibility on business users, organizations should establish robust data governance and management strategies in place.
5. Advanced Analytics
With the exponential growth of data, organizations should adopt predictive and prescriptive analytics to transform raw data into meaningful and actionable insights. Traditional analytics like descriptive and diagnostic analytics can only provide historical insights and cannot harness the much needed unstructured data.
Powered with AI and machine learning, advanced analytics like predictive and prescriptive analytics harness massive amounts of data and enable a forward looking approach for decision-makers. These technologies not only predict the future outcomes but also recommend the best course of action. Decision makers can spend less time analyzing data and more time gaining meaningful intelligence from it.
Conclusion
The current business intelligence landscape is changing rapidly. Most organizations today are able to accumulate a staggering amount of data – however, only a few of them are able to convert this data into insights which drive business decisions. As the importance of data increases day by day, companies need to fill in any gaps and augment their existing BI system, and swiftly adopt modern technologies of BI and analytics to make data work!
