Every new year the people make their predictions for the future and also comment on trends. I've read many posts and articles about predictions and trends for 2010 on business intelligence and performance management. Below is a summary of some that I found most interesting:
1 - We will witness the emergence of packaged strategy-driven execution applications. As we discussed in Driven to Perform: Risk-Aware Performance Management From Strategy Through Execution (Nenshad Bardoliwalla, Stephanie Buscemi, and Denise Broady, New York, NY, Evolved Technologist Press, 2009), the end state for next-generation business applications is not merely to align the transactional execution processes contained in applications like ERP, CRM, and SCM with the strategic analytics of performance and risk management of the organization, but for those strategic analytics to literally drive execution. We called this "Strategy-Driven Execution", the complete fusion of goals, initiatives, plans, forecasts, risks, controls, performance monitoring, and optimization with transactional processes.
2 - The holy grail of the predictive, real-time enterprise will start to deliver on its promises. While classic analytic tools and applications have always done a good job of helping users understand what has happened and then analyze the root causes behind this performance, the value of this information is often stale before it reaches its intended audience.
3 - The industry will put reporting and slice-and-dice capabilities in their appropriate places and return to its decision-centric roots with a healthy dose of Web 2.0 style collaboration.
4 - Performance, risk, and compliance management will continue to become unified in a process-based framework and make the leap out of the CFO’s office. The disciplines of performance, risk, and compliance management have been considered separate for a long time, but the walls are breaking down.
5 - SaaS / Cloud BI Tools will steal significant revenue from on-premise vendors but also fight for limited oxygen amongst themselves.
6 - The undeniable arrival of the era of big data will lead to further proliferation in data management alternatives.
7 - Advanced Visualization will continue to increase in depth and relevance to broader audiences.
8 - Open Source offerings will continue to make in-roads against on-premise offerings. Much as Saas BI offerings are doing, Open Source offerings in the larger BI market are disrupting the incumbent, closed-source, on-premise vendors.
9 - Data Quality, Data Integration, and Data Virtualization will merge with Master Data Management to form a unified Information Management Platform for structured and unstructured data.
10 - Excel will continue to provide the dominant paradigm for end-user BI consumption. For Excel specifically, the number one analytic tool by far with a home on hundreds of millions of personal desktops, Microsoft has invested significantly in ensuring its continued viability as we move past its second decade of existence, and its adoption shows absolutely no sign of abating any time soon.
- Self-service operational BI puts information workers in driver’s seat: Enterprises have begun to adopt self-service BI to cut costs, unclog the analytics development backlog, and improve the velocity of practical insights. Users are demanding tools to do interactive, deeply dimensional exploration of information pulled from enterprise data warehouses, data marts, transactional applications, and other systems. In 2010, users will flock to self-service BI offerings as the soft economy keeps pressure on IT budgets. In the coming year, BI software as a service (SaaS) subscription offerings will be particularly popular, in a market that has already become fiercely competitive. So will the new generation of BI mashup offerings for premises-based deployment.
- User-friendly predictive modeling comes to the information workplace: Predictive analytics can play a pivotal role in day-to-day business operations. If available to information workers—not just to Ph.D. statisticians and professional data miners—predictive modeling tools can help business people continually tweak their plans based on flexible what-if analyses and forecasts that leverage both deep historical data as well as fresh streams of current event data. In 2010, user-friendly predictive modeling tools will increasingly come to market, either as stand-alone offerings or as embedded features of companies’ BI environments.
- Advanced analytics sinks deep roots in the data warehouse: Advanced analytics demands a high-performance data management infrastructure to handle data integration, statistical analysis, and other compute-intensive functions. In 2010, in-database analytics will become a new best practice for data mining and content analytics, in which the enterprise data warehousing professionals must now collaborate closely with the subject matter experts who build and maintain predictive models. To support heterogeneous interoperability for in-database and in-cloud analytics, open development frameworks-- especially MapReduce and Hadoop—will be adopted broadly by data warehousing and analytics tools vendors. In the coming year, we’ll also see the beginning of an industry push toward an open development framework for inline predictive models that can be deployed to CEP environments. Clearly, in-CEP predictive analytics will be a critical component of truly adaptive BAM for process analytics.
- Social network analysis bring powerful predictive analysis to the online economy: Social network analysis thrives on the deepening streams of information—structured and unstructured, user-generated and automated—that emanate from Facebook, Twitter, and other new Web 2.0 communities. In the coming year, many vendors of predictive modeling tools will enhance their social network analysis features to support real-time customer segmentation, target marketing, churn analysis, and anti-fraud.
- Low-cost data warehousing delivers fast analytics to the midmarket: Though enterprises can certainly do BI without a data warehouse, this critical infrastructure platform is essential for high-performance reporting, query, and analytics against large data sets. In 2010, many data warehousing vendors will lower the price of their basic appliance products to less than $20,000 per usable terabyte.At the same time, enterprises will see a growing range of cost-effective solution appliances in 2010, combined DW appliances with preconfigured BI, advanced analytics, data cleansing, industry information models, and other data management applications and tools.
- Data warehousing virtualizing into the cloud: The data warehouse, like all other components of the BI and data management infrastructure, is entering the cloud. In 2010, we’ll see vendors continue to introduce cloud, SaaS, and virtualized deployments of their core analytic databases.
Howard Dresner wrote a nice and interesting post in his blog, called A thought (or two) for the New Year. He asked a question: why do we (still) struggle to effectively use information to make better decisions and what can we do to improve?, and told about five ideas that might help:
1 - Get the culture right: If a culture is not receptive to BI and EPM, those efforts will have limited impact. This is the basis for my latest book, Profiles in performance – Business Intelligence Journeys and the Roadmap for Change. In it I assert that organizations need to establish a “performance-directed culture” first – as a context or rationale for these solutions. To this end, I developed the Performance Culture Maturity Model (Patent Pending) and related methodologies for assessing an organization’s culture and offering a path to becoming more “performance-directed”.
2 - Don’t get overly enamored with technology: This is not to say that technology isn’t important. You certainly will want to have appropriate technology once you have the right environment in place to use it. However, it’s a means to an end, not an end in itself and large sums of money can be wasted with a “technology-led” strategy.
3 - Get strategic: There was a time when many/most organizations had “strategic planning” functions. They were chartered to think and plan for the future – developing multiple scenarios and associated action plans. Today, few organizations have this sort of a function and it shows. Most organizations have allowed themselves to become overwhelmingly tactical and reactive in nature.
4 - Get the metrics right: Assuming we have a well defined and communicated mission and strategy, we can use metrics as a means of measuring and managing execution. This is where things get complex and there’s a real risk of providing large quantities of information with little impact. Here’s where “less is more”. Metrics need to be focused upon alignment with the strategy in a way that they’re actionable.
5 - Take action: Many of us either engage in “analysis paralysis” or rely upon intuition when faced with a critical decision. Instead, we should view Business Intelligence and associated analyses as part of a learning process – which uses information to inform our decision-making, but doesn’t make the decision for us. This requires taking calculated risks, since information will typically be incomplete. However, the former two scenarios expose the organization to completely unknown risks. So, frame the decision to be made. Collect and analyze enough information/facts to build workable assumptions. Assess the benefits, risks, and alternatives and make your decision. Finally, monitor the impact and adjust if possible and as needed.
- In-memory will be a key theme this year as Microsoft will ship Gemini, SAP opens up BW Accelerator, IBM Cognos increasingly leverages TM1, and MicroStrategy 9 OLAP Services gains traction. In-memory approaches are not only key to BI platforms but also to any analysis that involves both speed and analytic complexity (Spotfire, SAS JMP, QlikView). The winners in this are the customers; the losers will be the vendors who have no strategy in this space or where in-memory is their only differentiator.
- Cloud computing and SaaS will become less niche as both BI heavy weights and vertically-focused vendors recognize that the infrastructure side of BI offers little competitive advantage; instead, it's the time-to-value and agility. IT owners who don't want to give up any control are in for a bruising.
- SMBs will embrace BI but, faced with a myriad of good BI tool choices, these customers will choose products from vendors who offer better service, clarity of value, a partnership mentality, and at the least cost.
- The enterprise vs. departmental BI debate will continue but will be tempered with the reality of "best" and "right" doesn't matter if you get outsourced, laid off, or go bankrupt. Those burned by over spending on software will look for IT to offer some enterprise restraint. The wiser of the industry will find an ideal balance of having an enterprise focus on those items that bring economies of scale and synergies, while departmentalizing those aspects in which differentiation and time to value matter more.
- Got dashboards? This category of tools only keeps getting better. Dashboards will become as commonplace as reporting and ad hoc query capabilities; but in 2010, they will be more animated, better integrated, packing more effective insights, on whatever device users prefer (including the iPhone and Droid).
- Good data, bad decisions remain BI's biggest problem. I'd like to be optimistic and think that we will rid the BI industry of all that ails it, but the world economy, corruption in politics, the epidemic of overweight people while others starve -- you name it -- tell me that human nature will continue to sabotage even the best of BI deployments.
- Social networking and sentiment analysis should be on everyone's radar. Now that it seems every company has a Facebook presence (maybe for marketing, maybe for customer support), the need for sentiment analysis grows. So all those tweets, blogs, and social network updates only add to the data explosion and sense of information overload.