Business Intelligence should help organizations improve business outcomes by making informed decisions. The problem is that Business Intelligence is the overarching term applied to the tools, technologies, and best practices that that supposedly help organizations make sense of data. Where should you start? What tools should you use? What are the best practices? How do you manage the mass of data flowing into your organization? To which buzzwords should you pay attention? Perficient’s Enterprise Information Solutions group helps organizations determine how to put business and intelligence back into Business Intelligence.
In a previous post, I looked at Business Intelligence and what it means today. In this post, I examine some of the trends we are seeing today.
Many of the complaints that echo around the halls of Business Intelligence relate to the lack of agility and responsiveness of IT driven implementations. To address this, users will increasingly gravitate to Business Intelligence tools that allow Data Discovery. These tools have no hard and fast, rigid data sources and structures. They allow the end-user to quickly plug-in, model, and analyze new data sources while still leveraging enterprise metadata and data.
As the Internet has grown, so has a user’s expectation of simple mechanisms for finding information. Search-based business intelligence tools will become the norm allowing users to bring together both structured and unstructured data using search terms. Search-based business intelligence tools have a “Google-like” interface allowing users to explore data with little formal training; they gather data from disparate sources with little need for a previously constructed semantic layer; using RAM and specialized indexing helps to improve the performance of queries. The user interfaces of these tools use text and natural language to help users find the information they need.
Another consumer trend that will drive Business Intelligence forward is social media and collaboration. Collaborative Business Intelligence allows users to find, discuss, and rank the data, reports, and analyses that they find the most useful. They have simple portal based interfaces and rely on the search capabilities previously discussed. The tools provide ranking mechanisms and recommendation based on a user’s previous consumption, “likes”, and profile. The tools allow users to share information, ask for feedback, provide commentary, and be notified based on the preferences set
The previous three trends all point towards Business Intelligence’s Holy Grail, Self-service Business Intelligence. IT will continue to provide the platforms and integration components for Business Intelligence. IT will also be heavily involved in the mass distribution of standardized information. Business users and analysts will become explorers and “data scientists” looking for the insights Business Intelligence can provide.
Taking the current real-time monitoring of Dashboarding into the future is Operational Intelligence. Complex event processing is used to combine data from multiple sources to identify events or patterns. These are fed through the Operational Intelligence applications to analyze them and respond to them in near real-time. Operational intelligence allows organizations to identify anomalies, opportunities, and threats initiating the “best next action” for optimal business impact. Operational Business Intelligence allows organizations to enable Pervasive
Business Intelligence with Context
Awareness. That is front-line employees are enabled with Business Intelligence that is directly connected to the applications they are using without the need to formulate queries or request information.
Organizations will continue to adopt and deploy Mobile Business Intelligence. The latest tools will allow them to move beyond delivery of simple descriptive Business Intelligence, and on to full interaction. Users will be able to explore the data on their device drilling up and down, and slicing and dicing the data. Mobile Business Intelligence will allow remote users, and those away from their desk, to gain access to information wherever they are and make decisions immediately. The debate will continue as to whether to deliver in native or web-based applications. No matter what, interactivity is the true key to Mobile Business Intelligence.
As it always has, Business Intelligence will depend on the consumption of data. This includes the consumption of Big Data. No matter how many “V”s come to define Big Data the initial three volume, variety, and velocity will always play a part. The standard tools will continue to be adapted to ingest data from the likes of Hadoop, Cassandra, and BigQuery. Business Intelligence solutions that do not include this capability will quickly be superseded by those that do.
Enterprises will adopt Cloud Business Intelligence in its many forms. Some organizations will adopt the Software-as-a-Service model simply using the provider’s Business intelligence applications running in the cloud. Other organizations will adopt the Platform-as-a-Service model leveraging the provider’s platform to build their own Business Intelligence applications. Yet other organizations will leverage the Infrastructure-as-a-Service model, deploying their own platforms and applications on top of the hosting infrastructure. Initially, the focus will be on consuming cloud based data sources and rapid deployment of development environments but as comfort and security protocols improve, more internal data and critical applications will be moved to the cloud.
Analytic Appliances will feature heavily in future Business Intelligence implementations. Analytic Appliances bring together multiple tools and technologies into a single highly integrated and optimized machine. Analytic Appliances provide users with transparent access to multiple data sources, including historical data warehouses and real-time operational database, Big Data sources, etc. allowing them to perform in depth analysis. Often Analytical Appliances offer pre-packaged ready-to-run analytical functions such as digital marketing optimization, social network analysis, fraud detection, and financial analysis.
Prescriptive Analytics moves Predictive Analytics into the future. It helps organizations do decide the best action to take based on the current situation, the business’ requirements and goals, and any constraints that exist. Prescriptive Analytics takes in structured and unstructured data and uses business rules along with mathematical and computational models to predict what lies ahead and prescribe how to take advantage without compromising other objectives. Prescriptive Analytics continuously and automatically tries to anticipate the what, when, and why of unknown future events.
I will close out this trilogy up with another post on how Perficient is helping organizations achieve these Business Intelligence objectives.