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<\/p>\nHow is it that industry leaders like Blockbuster, Eastman Kodak, and Digital Equipment meet their ultimate demise at the hands of competitors that were once a mere glimpse in their rearview mirror? \u00a0<\/p>\n
Today, it\u2019s more clear than ever that companies must innovate to stay on top. \u00a0Lucky for us, data and technology provide the perfect medium for companies to innovate.<\/p>\n
There are many reasons why great companies fail, but most can be summarized by their failure to innovate. \u00a0As corporations grow in size, they are less adaptive by nature and take fewer chances on unsuspecting products or small markets. \u00a0Unfortunately, their seemingly nonthreatening competitors will take full advantage of these passed over opportunities and grow them into large profits. \u00a0By the time these corporations realize the product or market is now big enough to interest them, it\u2019s too late. \u00a0Because of this, senior leadership now makes innovation a top priority.<\/p>\n
Innovation is by no means easy to accomplish, especially for large corporations. Corporations are typically filled with bureaucracy, arrogance, short-term investment strategies, and inadequate skills. \u00a0On top of that, let\u2019s face it, it\u2019s pretty hard to come up with the next best thing, right?<\/p>\n
Business process innovation through data and technology is a form of innovation that is much more tangible than your general product innovation. \u00a0It is achieved through the culmination of what I call, \u201cThe Four Pillars of Data Innovation\u201d which include: Internal and External Data, Technology Platforms, Highly Skilled Technical Individuals, and Business Visionaries. \u00a0When done correctly, companies have the ability to maximize profits, recognize hidden opportunities, and to stay competitive.<\/p>\n
Internal and External Data<\/strong> External Data is often overlooked and greatly undervalued despite its high availability, increasing accuracy, and relatively low cost to obtain. Marketers are very familiar with lists of consumers and businesses and the myriad of attributes of which they can filter to accurately target their audience. \u00a0Examples of underutilized external data sets include Currency Rates, Oil Prices, Commodity Futures, and more.<\/p>\n Technology Platforms<\/strong> First thing\u2019s first, Business Intelligence Platforms are built, not bought. There are many great offerings on the market today specializing in particular areas. Once the various components are selected, the hard part of building the platform around the organization\u2019s business processes and needs begins.<\/p>\n Highly Skilled Technical Individuals<\/strong> Business Visionaries<\/strong> \u201cWhat has happened\u201d vs. \u201cWhat will happen\u201d<\/em><\/p>\n I break Business Process Innovation through Data and Technology into two parts; \u201cWhat has <\/em>happened\u201d and \u201cWhat will happen\u201d.<\/em><\/p>\n Let\u2019s start with \u201cWhat has <\/em>happened\u201d. \u00a0This is your basic reporting used by all levels of management from General Sales and P&L to Operational Efficiencies and Customer Satisfaction. \u00a0There isn\u2019t anything mind-blowing here, but worth noting are a few negative themes among corporations. \u00a0Inflexibility, inability to slice and dice data, inability to combine data from different data sources, excel workbooks upon excel workbooks, lack of historical data, data quality, and a lack of \u201cone source of truth\u201d are just a few that come to mind. \u00a0The fact is, most corporation\u2019s analytical capabilities can best be described as a hodgepodge of efforts that have accumulated over the years out of necessity without a clear plan or strategy. \u00a0When million dollar decisions are being made, it\u2019s critical that the technologies in place are homogenous and data is accurate.<\/p>\n Moving on, let\u2019s talk about \u201cWhat will <\/em>happen\u201d. \u00a0When asking this question, we are basically talking about Predictive Analytics. Companies like Pandora and Netflix have been using Predictive Analytics for years. \u00a0These innovative companies have created markets off the backs of their Predictive Analytic Engines that consumers value for their ability to make product recommendations. Insurance companies are no stranger to this line of thinking as well. \u00a0For years, they have been creating risk models to determine the rate to charge individuals based on attributes and behavior of a larger population.<\/p>\n Some uses of Predictive Analytics are not so obvious, particularly, to those outside the world of finance. \u00a0For example, Predictive Analytics has the potential to provide greater accuracy in forecasts. \u00a0Being able to better anticipate future prices of raw materials like crude oil or consumer demand has immense value. \u00a0Managing cash is another example of how Predictive Analytics can be used, especially for global companies dealing with multiple exchange rates.<\/p>\n From a Sales\/Operations side, it\u2019s even possible to anticipate when a customer may leave or a sales opportunity might fall out of the pipeline through Text and Interaction Mining on the fly. \u00a0Real-time pricing models based on supply and demand or peak\/non-peak hours is also possible.<\/p>\n Final Thoughts<\/strong><\/p>\n There are many new products available to help companies innovate from data visualization tools like Tableau<\/a> to data storage like Microsoft Azure<\/a>. Companies must realize the ROI of building (or rebuilding) their technology platforms and continue to innovate as the technology around them progresses. Once companies have a solid platform in place that allows them to address \u201cWhat has<\/em> happened\u201d they need to start looking into \u201cWhat will<\/em> happen\u201d. \u00a0If all is done correctly, they will soon become a Data-Driven Enterprise and have the ability to not only remain competitive but drive growth.<\/p>\n
\nInternal data is at the forefront of all companies, big or small. Computer applications that assist in CRM, ERP, Finance, Accounting, Web, and Marketing are generating this data in large quantiles.<\/p>\n
\nPrimary Technology Platforms are those software applications\u00a0mentioned above. \u00a0Unfortunately, they are too often implemented as \u201cclosed systems\u201d. These platforms need to become more dynamic and integrated. \u00a0Secondary technology platforms are those that include the software applications used to: \u00a0extract, transform, load, store, report, analyze, and even act on internal and external data. For the purpose of this article, I will group and refer to these as a \u201cBusiness Intelligence Platform\u201d.<\/p>\n
\nIn order to make use of internal and external data and build a Business Intelligence Platform, you need a team of highly skill technical individuals. \u00a0These guys are a special breed; they tend to come from a background of business analysis or software development. \u00a0Regardless of where they came from, they have strong business intuition, unrivaled technical skills, and great problem solving capabilities. Unfortunately, attracting this type of talent is easier said than done as they are in short supply. \u00a0It\u2019s crucial for organizations to have an ongoing effort to obtain, develop, and keep these individuals when they find them.<\/p>\n
\nBusiness visionaries are crucial to Innovating with data and technology. \u00a0They tend to have many years of industry experience and a firm understanding of technology. \u00a0Most importantly, they are creative and tend to work outside-of-the-box. \u00a0It\u2019s hard to say exactly where to find these guys, but more times than not, they are in a business analysis role or even supporting the technology platforms we just mentioned. \u00a0When you spot one in your organization, work with them and listen to what they have to say. \u00a0As crazy as some ideas may seem, with innovation, there are no bad ideas.<\/p>\n