This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Pimcore CEO Dietmar Rietsch offers a detailed outline of business intelligence basics and the future of data-driven intelligence.
With data growing in monumental proportions (at a rate of 463 exabytes per day by 2025), it is safe to say that data is omnipresent and increasing exponentially – powering everything we do. However, in reality, data alone is not enough to produce successful business outcomes. Clearly, the rate of data comprehension cannot keep up with that of its creation.
Today, “Data-driven” has turned into an attribute that every organization aspires to be recognized with. What it essentially means is data, as a true source of competitive differentiation, is imperative to arrive at better business decisions. In doing so, enterprises must harness the potential of their data through visualization tools, knowledge extraction, analytical models, and decision systems to extract the maximum amount of insights. In essence, they need Business Intelligence (BI).
A term introduced by the Gartner Group in the mid-90s, BI represents a wide area of applications and technologies that work in cohesion to collect, store, analyze, and provide access to information for improved business decision-making and business process modeling. At a time when as low as only 27 percent of data and analytics projects are translating into actionable insights, BI has become a de facto choice for enterprises to drive change. This large umbrella of BI that encompasses technologies, methodologies, processes, and architectures can help enterprises transform raw data into meaningful and useful information for driving informed business decisions.
To put it succinctly, data is the answer to the many pressing challenges in an enterprise, whereas BI is all about asking the right questions and drawing th right conclusions. BI helps to glean insights, uncover issues, track performance, spot opportunities, optimize processes, draw data-led comparisons, find ways to boost sales, discover newer ways to innovate, and forecast success.
Enterprises across industries depend on BI to fuel growth and future-proof themselves. Here’s an attempt to shine a light on some of the basics of BI:
Understanding Data and Information
To truly understand BI, one must know that it is neither a technology nor a product, and it’s not just about “insights” or a “single source of the truth.” In fact, it is a cluster of processes, architectures, and technologies that collectively contribute to informed decision-making. The overall objective of BI is to make sense of the vast amount of data accumulated within the information systems of enterprises using data analytics, data mining, data visualization, advanced statistics, process analysis, performance benchmarking, and descriptive analytics. BI changes the decision-making approach significantly, wherein data is leveraged to answer questions on an enterprise’s past, present, or even future.
Be it evaluating the performance of marketing campaigns, visualizing website traffic, or targeting potential customers in specific demographic segments, enterprises can dig into the specifics of data to discover current patterns and forecast future ones. At the core, BI “decodes” these patterns using models and algorithms and breaks the results down into actionable language. It is an interactive and approachable process that provides access to data to multiple levels of users, giving them an important nucleus to visualize data, obtain and comprehend insights, and create reports through customized dashboards.
Data Collection and Transformation
Essentially the first step to BI, data collection is about getting access to accurate and relevant data. Qualitative collection techniques are instrumental in unlocking the maximum potential of BI. To begin with, data in its raw form cannot be used directly for decision-making purposes. As such, BI enables data processing with appropriate extraction tools and analytical methods to transform data into information and knowledge ready for decision-makers to act on. This data mainly originates from administrative, logistical, and commercial transactions, as well as from external sources.
Data can be ingested from and delivered to diverse sources and destinations. The type of data powering BI platforms can vary from paper-based reports to telephonic conversations, e-mail chats, files stored on a computer, or portions of audio/video files. It can also be data about the data, i.e., the metadata. Following a data pipeline, enterprises perform Extract, Transform, and Load (ELT) operations to process structured, unstructured, and semi-structured data and then transfer it to cloud data lakes or warehouses – one of the main data sources for BI applications.
This tenet of the BI architecture facilitates the design and use of optimal flows in line with the evolving business requirements. 67 percent of enterprises depend on data integration to support analytics and BI platforms today. As a significant aspect of data warehousing, data integration enables improved communication and effective collaboration required for supporting decision-making and, by extension, better outcomes. In many instances, decision support systems distributed across an enterprise or externally are required to access information. These disparate systems are tightly knit through data warehouse integration, thereby facilitating access to information.
This results in a unified view of data for accurate evaluation, thereby simplifying the BI processes of analysis to derive actionable information on the current state of the business. Herein, enterprises can achieve data integration with uniform encoding methods, conversion of standard measurement units, and a semantic homogeneity of information. That said, the cloud-based architecture supports an effective data integration strategy to normalize data across multiple systems and platforms and accomplish a single source of truth, which, in turn, allows for real-time decision-making.
Resolving Data Discrepancies
Quality and usability are two critical characteristics of data driving the success of any BI strategy. However, data discrepancies lead to misinformation and incorrect decisions that hinder the process. These discrepancies often stem from distributed, autonomous, and possibly heterogeneous information in data warehouses. Data collected from different time zones and geolocation, multiple updates, comparing events, and other similar scenarios can be inaccurate or misaligned, which can potentially render the BI system unusable. Enterprises need a systematic process of addressing manual and system-generated discrepancies produced within a report or a study.
In essence, the process of empowering enterprises with BI ensures that the data is complete, accurate, and compliant with specific protocols. This is achieved through a BI repository for master data management, data hygiene, and data governance. Moreover, enterprises must have data management policies in place that are updated periodically to ensure data consistency during the analysis. Modern BI systems eliminate discrepancies in the data definitions to facilitate faster and more accurate decisions.
Uncovering Trends and Inconsistencies
As mentioned earlier, BI involves data mining that utilizes automation to enable faster analysis and identification of patterns and outliers. These patterns pave the way for insights, trends, and recommendations on the current state of business. BI tools often feature several types of data modeling and analytics – including exploratory, descriptive, statistical, and predictive – that further explore data, predict trends, and make recommendations. The overarching process of understanding trends and eliminating inconsistencies is often backed by data modeling. Interestingly, modeling the data mitigates errors while improving data integrity. It also significantly accelerates speed and enhances the performance of data retrieval and analytics.
Clearly defined data simplifies the analysis and mapping of relationships between data attributes. Moreover, while 70 percent of enterprises view data discovery and visualization as vital for their businesses, data modeling plays a critical role in these processes, thereby simplifying trends, patterns, and data relations identification and comprehension that is essential for decision making.
The Time to Instill Intelligence in Businesses is Now
On a concluding note, it is hard to ignore the advantages that BI brings to the table. BI is fast gaining prominence in a data-driven world to boost ROI in businesses, present new growth opportunities, encourage smart, fact-based decision-making, improve business productivity, and facilitate access to critical information. The proof is in the pudding: The global BI market size is expected to snowball to $43.03 billion in 2028 at a CAGR of 8.7 percent in the 2021-2028 period.
As businesses continue to reel under the repercussions of the pandemic, there has been a renewed focus on exploring and adopting new business models, maintaining an equilibrium between physical and digital competencies. That said, the demand for data analytics has skyrocketed in order to capitalize on real-time data across diverse sources. As a result, cloud-based BI is rapidly maturing and witnessing increased acceptance in businesses.
This already-tremendous asset is maximizing its potential with the power of more recent technologies like Artificial Intelligence (AI). BI, coupled with AI and machine learning, can help businesses handle and process large sets of data – both structured and unstructured – and perform data analytics to find connections and insights in an easier and much faster way. Touted as the future of analytics in businesses, AI-enabled BI will be a game-changer at a time when 86percent of enterprises look forward to making AI a “mainstream technology.” Businesses must watch this space for identifying and seizing new growth opportunities.