Bottom line: Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.
Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.
Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey.
The ten ways big data is revolutionizing supply chain management include:
- Enabling more complex supplier networks that focus on knowledge sharing and collaboration as the value-add over just completing transactions. Big data is revolutionizing how supplier networks form, grow, proliferate into new markets and mature over time. Transactions aren’t the only goal, creating knowledge-sharing networks is, based on the insights gained from big data analytics. The following graphic from Business Ecosystems Come Of Age (Deloitte University Press) (free, no opt-in) illustrates the progression of supply chains from networks or webs, where knowledge sharing becomes a priority.
- Big data and advanced analytics are being integrated into optimization tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace. These are the top four supply chain capabilities that Delotte found are currently in use form their recent study, Supply Chain Talent of the Future Findings from the 3rd Annual Supply Chain Survey (free, no opt-in). Control tower analytics and visualization are also on the roadmaps of supply chain teams currently running big data pilots.
- 64% of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations. SCM World’s latest Chief Supply Chain Officer Report provides a prioritization of the most disruptive technologies for supply chains as defined by the organizations’ members. The following graphic from the report provides insights into how senior supply chain executives are prioritizing big data analytics over other technologies.
- Using geoanalytics based on big data to merge and optimize delivery networks. The Boston Consulting Group provides insights into how big data is being put to use in supply chain management in the article Making Big Data Work: Supply Chain Management (free, opt-in). One of the examples provided is how the merger of two delivery networks was orchestrated and optimized using geoanalytics. The following graphic is from the article. Combining geoanalytics and big data sets could drastically reduce cable TV tech wait times and driving up service accuracy, fixing one of the most well-known service challenges of companies in that business.
- Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives. Supply chain visibility often refers to being able to see multiple supplier layers deep into a supply network. It’s been my experience that being able to track financial outcomes of supply chain decisions back to financial objectives is attainable, and with big data app integration to financial systems, very effective in industries with rapid inventory turns. Source: Turn Big Data Into Big Visibility.
- Traceability and recalls are by nature data-intensive, making big data’s contribution potentially significant. Big data has the potential to provide improved traceability performance and reduce the thousands of hours lost just trying to access, integrate and manage product databases that provide data on where products are in the field needing to be recalled or retrofitted.
- Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data. IBM has developed a quality early-warning system that detects and then defines a prioritization framework that isolates quality problem faster than more traditional methods, including Statistical Process Control (SPC). The early-warning system is deployed upstream of suppliers and extends out to products in the field.
and 85.40% for Computer and Information Research Scientists.
Demand for Python programming expertise increased 96.9% in big-data related positions in the last twelve months.
These and other key insights are from a recent analysis completed of big data hiring trends using WANTED Analytics, the leading provider of data analytics on the workplace. For purposes of this analysis, the term “big data” is comprised of the four skill sets of data analysis, data acquisition, data mining and data structures. The WANTED Analytics taxonomy references these skill sets when queries are made on the term “big data”.
The company currently maintains a database of more than one billion unique job listings and is collecting hiring trend data from more than 150 countries. WANTED Analytics has never been a client, they provided complimentary access based on my requesting a trial account. Many Forbes readers are interested in staying current on big data hiring trends, which led me to complete this analysis.
Key Take-aways include the following:
- Demand for big data expertise across a range of occupations saw significant growth over the last twelve months. There was a 123.60% jump in demand for Information Technology Project Managers with big data expertise, and an 89.8% increase for Computer Systems Analysts. The following table provides an overview of the distribution of open positions by occupation and the percentage growth in job demand over time.
- The five leading industries with the most job openings requiring big data expertise include Professional, Scientific and Technical Services (27.14%), Information Technologies (18.89%), Manufacturing (12.35%), Retail Trade (9.62%) and Sustainability, Waste Management & Remediation Services (8.20%). The following graphic shows the distribution of open positions between September 1, 2014 to today, December 29, 2014:
- The Hiring Scale is 76 for jobs that require big data skills with 12 candidates per job opening as of December 29, 2014. The higher the Hiring Scale score, the more difficult it is for employers to find the right applicants for open positions. Nationally an average job posting for an IT professional with cloud computing expertise is open just 47 days.
- The median salary for professionals with big data expertise is $103,000 a year. Sample jobs in this category include Big Data Solution Architect, Linux Systems and Big Data Engineer, Big Data Platform Engineer, Lead Software Engineer, Big Data (Java, Hadoop, SQL) and others. The distribution of median salaries across all industries shown below:
- San Jose – Sunnyvale – Santa Clara, CA, San Francisco – Oakland – Fremont, CA, and Washington – Arlington – Alexandria, DC are the top three U.S. employment markets for big data related jobs as of today. Mapping the distribution of job volume, salary range, candidate supply, posting period and hiring scale by Metropolitan Statistical Area (MSA) or states and counties is supported by WANTED Analytics and shown in the following graphic. A summary of the top twenty employment markets is also shown following the map:
- Cisco (NASDAQ:CSCO), IBM (NYSE: IBM) and Oracle (NYSE:ORCL) have the most open big data-related positions today. Cisco, its supplier, partner and support ecosystem companies have 3,613 related big data positions available. The following table shows the top ten big data employers today, the distribution of jobs, and the number of new jobs added over the last year.
- Python programming (96.90%), Linux expertise (76.60%) and Structured Query Language (SQL) (76%) are the three most in-demand skills in positions that mention big data as a requirement. The following table provides an overview of the top 10 most in-demand skills:
McKinsey & Company recently published How Big Data Can Improve Manufacturing which provides insightful analysis of how big data and advanced analytics can streamline biopharmaceutical, chemical and discrete manufacturing.
The article highlights how manufacturers in process-based industries are using advanced analytics to increase yields and reduce costs. Manufacturers have an abundance of operational and shop floor data that is being used for tracking today. The McKinsey article shows through several examples how big data and advanced analytics applications and platforms can deliver operational insights as well.
The following graphic from the article illustrates how big data and advanced analytics are streamlining manufacturing value chains by finding the core determinants of process performance, and then taking action to continually improve them:
Big Data’s Impact on Manufacturing Is Growing
In addition to the examples provided in the McKinsey article, there are ten ways big data is revolutionizing manufacturing:
- Increasing the accuracy, quality and yield of biopharmaceutical production. It is common in biopharmaceutical production flows to monitor more than 200 variables to ensure the purity of the ingredients as well as the substances being made stay in compliance. One of the many factors that makes biopharmaceutical production so challenging is that yields can vary from 50 to 100% for no immediately discernible reason. Using advanced analytics, a manufacturer was able to track the nine parameters that most explained yield variation. Based on this insight they were able to increase the vaccine’s yield by 50%, worth between $5M to $10M in yearly savings for the single vaccine alone.
- Accelerating the integration of IT, manufacturing and operational systems making the vision of Industrie 4.0 a reality. Industrie 4.0 is a German government initiative that promotes automation of the manufacturing industry with the goal of developing Smart Factories. Big data is already being used for optimizing production schedules based on supplier, customer, machine availability and cost constraints. Manufacturing value chains in highly regulated industries that rely on German suppliers and manufacturers are making rapid strides with Industrie 4.0 today. As this initiative serves as a catalyst to galvanize diverse multifunctional departments together, big data and advanced analytics will become critical to its success.
- Better forecasts of product demand and production (46%), understanding plant performance across multiple metrics (45%) and providing service and support to customers faster (39%) are the top three areas big data can improve manufacturing performance. These findings are from a recent survey LNS Research and MESA International completed to see where big data is delivering the greatest manufacturing performance improvements today. You can find the original blog post here.
- Integrating advanced analytics across the Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) framework to fuel continuous improvement. Getting greater insights into how each phase of a DMAIC-driven improvement program is working, and how the efforts made impact all other areas of manufacturing performance is nascent today. This area shows great potential to make production workflows more customer-driven than ever before.
- Greater visibility into supplier quality levels, and greater accuracy in predicting supplier performance over time. Using big data and advanced analytics, manufacturers are able to view product quality and delivery accuracy in real-time, making trade-offs on which suppliers receive the most time-sensitive orders. Managing to quality metrics becomes the priority over measuring delivery schedule performance alone.
- Measuring compliance and traceability to the machine level becomes possible. Using sensors on all machinery in a production center provides operations managers with immediate visibility into how each is operating. Having advanced analytics can also show quality, performance and training variances by each machine and its operators. This is invaluable in streamlining workflows in a production center, and is becoming increasingly commonplace.
- Selling only the most profitable customized or build-to-order configurations of products that impact production the least. For many complex manufacturers, customized or build-to-order products deliver higher-than-average gross margins yet also costs exponentially more if production processes aren’t well planned. Using advanced analytics, manufacturers are discovering which of the myriad of build-to-order configurations they can sell with the most minimal impact to existing production schedules to the machine scheduling, staffing and shop floor level.
- Breaking quality management and compliance systems out of their silos and making them a corporate priority. It’s time for more manufacturers to take a more strategic view of quality and quit being satisfied with standalone, siloed quality management and compliance systems. The McKinsey article and articles listed at the end of this post provide many examples of how big data and analytics are providing insights into which parameters matter most to quality management and compliance. The majority of these parameters are corporate-wide, not just limited to quality management or compliance departments alone.
- Quantify how daily production impacts financial performance with visibility to the machine level. Big data and advanced analytics are delivering the missing link that can unify daily production activity to the financial performance of a manufacturer. Being able to know to the machine level if the factory floor is running efficiently, production planners and senior management know how best to scale operations. By unifying daily production to financial metrics, manufacturers have a greater chance of profitably scaling their operations.
- Service becomes strategic and a contributor to customers’ goals by monitoring products and proactively providing preventative maintenance recommendations. Manufacturers are starting to look at the more complex products they produce as needing an operating system to manage the sensors onboard. These sensors report back activity and can send alerts for preventative maintenance. Big data and analytics will make the level of recommendations contextual for the first time so customers can get greater value. General Electric is doing this today with its jet engines and drilling platforms for example.
Additional sources of information on Big Data in Manufacturing:
- Attitudes on How Big Data will Affect Manufacturing Performance. Posted by Greg Goodwin on Thu, Mar 06, 2014. LNS Research. http://blog.lnsresearch.com/blog/bid/194972/Attitudes-on-How-Big-Data-will-Affect-Manufacturing-Performance-DATA From Value to Vision: Reimagining the Possible with Data Analytics, David Kiron, Renee Boucher Ferguson and Pamela Kirk Prentice, MIT Sloan Management Review, March 2013. http://sloanreview.mit.edu/reports/analytics-innovation/introduction/
- Merck Optimizes Manufacturing With Big Data Analytics, Information Week, Doug Henschen. April 2, 2014. http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/merck-optimizes-manufacturing-with-big-data-analytics/d/d-id/1127901
- Takeaways from the MIT/Accenture Big Data in Manufacturing Conference. Posted by Greg Goodwin on Wed, Nov 27, 2013. http://blog.lnsresearch.com/blog/bid/190482/Takeaways-from-the-MIT-Accenture-Big-Data-in-Manufacturing-Conference
- The Internet of Things and the future of manufacturing. McKinsey and Company. Executives at Robert Bosch and McKinsey experts discuss the technology-driven changes that promise to trigger a new industrial revolution. June 2013 | by Markus Löffler and Andreas Tschiesner. http://www.mckinsey.com/insights/business_technology/the_internet_of_things_and_the_future_of_manufacturing
- The Rise of Industrial Big Data: Leveraging large time-series data sets to drive innovation, competitiveness and growth — capitalizing on the big data opportunity, GE Intelligent Platforms White Paper, April 2012. http://www.ge-ip.com/library/detail/13170
- When Big Data Meets Manufacturing. Stephen Chick, Serguei Netessine, INSEAD Professors of Technology and Operations Management and Arnd Huchzermeier, Chaired Professor of Production Management at WHU-Otto Beisheim School of Management | April 16, 2014. http://knowledge.insead.edu/operations-management/when-big-data-meets-manufacturing-3297
Boston Consulting Group (BCG) recently released their fifth annual technology, media and telecommunications (TMT) value report. The 2013 TMT Value Creators Report: The Great Software Transformation, How to Win as Technology Changes the World (free, opt-in required, 41 pgs).
The five trends that serve as the foundation of this report include the increasing pervasiveness of software, affordable small devices, ubiquitous broadband connectivity, big-data analytics and cloud computing. BCG’s analysis illustrates how the majority of TMT companies that deliver the most value to shareholders are concentrating on the explosive growth of new markets, the rise of software-enabled digital metasystems, and for many, both.
The study is based on an analysis of 191 companies, 76 in the technology industry, 62 from media and 53 from telecom. To review the methodology of this study please see page 28 of the report.
Here are the key takeaways from this years’ BCG TMT Value Creators Report:
- BCG is predicting 1B smartphones will be sold in 2013, the first year their sales will have exceeded those of features phones. By 2018, there will be more than 5B “post-PC” products (tablets & smartphones) in circulation. There are nearly as many mobile connections in the world as people (6.8B) according to the United Nation’s International Telecommunication Union (ITU).
- 27 terabytes of data is generated every second through the creation of video, images social networks, transactional and enterprise-based systems and networks. 90% of the data that is stored today didn’t exist two years ago, and the annual data growth rate in future years is projected to be 40% to 60% over current levels according to BCG’s analysis.
- The ascent of communications speeds is surpassing Moore’s Law as a structural driver of growth. BCG completed the following analysis graphing the progression of microprocessor transition count (Moore’s Law) relative to Internet speed (bps) citing Butter’s Law of Photonics which states that the amount of data coming out of an optical fiber is doubling every nine months. BCG states that these dynamics are democratizing information technology and will lead to the cloud computing industry (software and services) reaching nearly $250B in 2017.
- BCG predicts that India will see a fivefold increase in digitally-influenced spending, ascending from $30B in 2012 to $150B in 2016, among the fastest of all nations globally according to their study. India will also see the value of online purchases increase from $8B in 2012 t5o $50B in 2016.
- 3D printing is forecast to become a $3.1B market by 2016, and will have an economic impact of $550B in 2025, fueling rapid price reductions in 3D printers through 2017. BCG sees 3D printing, connected travel, genomics and smart grid technologies are central to their digital metasystem. The following graphic illustrates the key trends in each of these areas along with research findings from BCG and other sources.
- Only 7% of customers are comfortable with their information being used outside of the purpose for which it was originally gathered.
- BCG reports that mobile infrastructure investments in Europe have fallen 67% from 2004 to 2014. Less than 1% of mobile connections in Europe were 4B as of the end of 2012, compared to 11% in the U.S. and 28% in South Korea. European operators have also been challenged to monetize mobile data as well, as the following figures illustrate.
- Big Data is attracting $19B in funding across five key areas according to BCG’s analysis. These include consumer data and marketing, enterprise data, analytical tools, vertical markets and data platforms. A graphical analysis of these investments is shown below.
Enterprises are beginning to change their buying behaviors based on the deployment speed, economics and customization that cloud-based technologies provide. Gartner cautions however that enterprises are far from abandoning their on-premise models and applications entirely for the cloud.
Based on an analysis of the Gartner Hype Cycle for Cloud Computing, 2012, the best results are being attained by enterprises that focus on a very specific strategy and look to cloud-based technologies to accelerate their performance. Leading with a strategic framework of goals and objectives increases the probability of cloud-based platform success. Those enterprises that look to cloud platforms only for cost reduction miss out on their full potential.
The Hype Cycle for Cloud Computing, 2012 is shown below:
Cloudwashing and Inflated Enterprise Expectations
While the hype surrounding cloud computing may have peaked, cloudwashing continues to cause confusion and inflated expectations with enterprise buyers. This just slows down sales cycles, when more straightforward selling could lead to more pilots, sales and a potentially larger market. Cloud vendors who have the expertise gained from delivering cloud platforms on time, under budget, with customer references showing results are starting to overtake those that using cloudwashing as part of their selling strategies.
Additional take-aways from the Gartner Hype Cycle for Cloud Computing include the following:
- Cloud Email is expected to have a 10% adoption rate in enterprises by 2014, down from the 20% Gartner had forecasted in previous Hype Cycles. This represents modest growth as the adoption rate of this category had been between 5 and 6% in 2011.
- Big Data will deliver transformational benefits to enterprises within 2 to 5 years, and by 2015 will enable enterprises adopting this technology to outperform competitors by 20% in every available financial metric. Gartner defines Big Data as including large volumes processed in streams, in addition to batch. Integral to Big Data is an extensible services framework that can deploy processing to the data or bring data to the process workflow itself. Gartner also includes more than one asset type of data in their definition, including structured and unstructured content. The Priority Matrix for Cloud Computing, 2012 is shown below:
- Master Data Management (MDM) Solutions in the Cloud and Hybrid IT are included in this hype cycle for the first time in 2012. Gartner reports that MDM Solutions in the Cloud is getting additional interest from Enterprise buyers as part of a continual upward trend of interest in MDM overall. Dominant vendors in this emerging area include Cognizant, Data Scout, IBM, Informatica, Oracle and Orchestra Networks, are among those with MDM-in-the-cloud solutions.
- PaaS continues to be one of the most misunderstood aspects of cloud platforms. The widening gap between enterprise expectations and experiences is most prevalent in this market. Gartner claims this is attributable to the relatively narrow middleware functions delivered and the consolidation fo vendors and service providers in this market.
- By 2014 the Personal Cloud will have replaced the personal computer as the center of user’s digital lives.
- Private Cloud Computing is among the highest interest areas across all cloud computing according to Gartner, with 75% of respondents in Gartner polls saying they plan to pursue a strategy in this area by 2014. Pilot and production deployments are in process across many different enterprises today, with one of the major goals being the evaluation of virtualization-driven value and benefits.
- SaaS is rapidly gaining adoption in enterprises, leading Gartner to forecast more than 50% of enterprises will have some form of SaaS-based application strategy by 2015. Factors driving this adoption are the high priority enterprises are putting on customer relationships, gaining greater insights through analytics, overcoming IT- and capital budget-based limitations, and aligning IT more efficiently to strategic goals.
- More than 50% of all virtualization workloads are based on the x86 architecture. This is expected to increase to 75% by 2015. Gartner reports this is a disruptive innovation which is changing the relationship between IT and enterprise where service levels and usage can be tracked.
Bottom line: Gartner’s latest Hype Cycle for Cloud Computing shows that when cloud-based platforms are aligned with well-defined strategic initiatives and line-of-business objectives, they deliver valuable contributions to an enterprise. It also shows how Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) are the catalysts of long-term market growth. The following slide from the presentation High-Tech Tuesday Webinar: Gartner Worldwide IT Spending Forecast, 2Q12 Update: Cloud Is the Silver Lining (free for download) also makes this point.
A good friend of mine recently became CIO of a financial services firm and was given his first major project last month: make the complete accounting, financial, and loan provider data and applications available 24/7 on any iPad or Android-based tablet from any office, at any time.
The majority of loan provider applications are cloud-based and his company is running NetSuite. His corporate office is in Asia and cloud-based applications made it possible for the company to launch and operate in California within months. He’s been given six months to transform this mobile vision into reality.
Another CIO of a major A&D manufacturer I recently visited wants vendors to challenge him more to get greater value from his investments in legacy data and ERP systems. Using ERP to run batch reports alone has nearly caused project schedules to slip, so the focus internally is on real-time system integration of project management and accounting systems. He’s also been given the task of revamping accounting and financial systems by October, 2012, and they just started late last year.
Gartner’s Hype Cycle for ERP
Considering these two extremes in the context of the Gartner Hype Cycle for ERP (shown below) and the recent report SaaS and Cloud ERP Trends, Observations, and Performance 2011 (free for download until January 9, 2012) published by Aberdeen last month several take-aways emerge.
- CIOs are under increasing pressure in 2012 to enhance, modify even replace existing ERP systems while standardizing technology across the enterprise at the same time. The most risk-averse way around this is to add applications to single instance ERP backbone systems, with analytics and Business Intelligence (BI) being the among the most in demand.
- Cloud-based ERP in the Enterprise and Small & Medium Businesses (SMB) are accelerating along the Hype Cycle faster than Gartner indicates. Enterprises are using Cloud-based ERP systems as part of their two-tier ERP system strategies due to the Total Cost of Ownership (TCO) and time-to-deploy advantages, and the flexibility of tailoring everything from user interfaces to workflows to their specific requirements. Highly specialized Cloud-based ERP suites including those from Plex Systems are gaining traction due to their expertise in specific industries and the compliance-related challenges inherent within them. In SMBs, the cost and time-to-deploy are two major drivers with concerns over security being the biggest impediment to growth. Gartner reports that they are seeing Cloud-based ERP adoption fastest in companies with fewer than 200 users overall.
- Cloud-based ERP systems most often considered in industries that have high variable costs, rapid transaction cycles and tend towards higher Return on Invested Capital (ROIC). Based on the research SaaS and Cloud ERP Trends, Observations, and Performance 2011 the industries who are the most willing to consider Cloud-based ERP versus on-premise are Financial Services (22% SaaS versus 44% on-premise); Healthcare (42% SaaS versus 58% on-premise); and Professional Services (56% SaaS versus 58% on-premise).
- Large companies (over $500M in annual revenue) using Cloud-based ERP systems are opting for hosted deployments managed by their ERP vendor (10%) or an independent 3rd party (11%), with just 2% relying on a SaaS platform. Aberdeen defined small organizations as those with annual sales under $50M, midsize organizations having annual sales of $50M – $500M. The following is from SaaS and Cloud ERP Trends, Observations, and Performance 2011:
- ERP mobility will be a dominant force from the shop floor to each sales call where quotes, orders and contracts deliver real-time order and pricing updates. How a given manufacturer chooses to sell is even more important than what they sell in many industries. Equipping manufacturing, quality assurance, production scheduling, procurement and sales to have immediate data on what’s going on with orders, customers and suppliers is critical. For the sales and service teams, real-time data is the fuel they run on. There’s a chronic time shortage in many, many companies right now, and bringing greater ERP mobility from the shop floor to the sales call will increasingly be seen as a means to lessen the time crunch. 2012 is the year where mobility gets real across the enterprise with solid performance numbers being generated as a result. For companies with large sales forces and service organizations, integrating to key ERP systems to gain real-time data will quickly lead to increased sales and higher gross margins on service and warranty repairs.
- Gartner predicts that by 2015 enterprises who are successfully using extreme information management strategies (Big Data) will outperform competitors in their industry sectors by 20% in every available financial metric. The following is the Priority Matrix for ERP, 2011 showing what Gartner believes to be transformational technologies and strategies in ERP.
During the last few weeks of completing a research project on buyer personas, a key finding of of just how quickly enterprises are switching out legacy reporting apps for SaaS-based analytics and Business Intelligence (BI) is emerging.
I’ve been interviewing sales VPs, sales operations directors, contract managers and CIOs. Of all these groups, the CIOs are providing valuable insight into the transition SaaS is going through in their enterprises.
Throughout this post I’ll correlate the interviewed CIOs’ comments back to a recent report Forrester published titled Understanding The Business Intelligence Growth Opportunity by Holger Kisker, Ph.D. Dr. Kisker’s findings support many of the insights gained from the research on personas completed to date.
Key Points from Persona Interviews and Forrester Study
- Forrester predicts the market for BI SaaS software will be $529M in 2011 growing to $2.4B in 2014. The report mentions that by 2012, up to 30% of companies will have some SaaS-based BI services. From the conversations with CIOs, I think this is actually low. The urgency to get off of legacy reporting systems and onto a unified reporting platform is quickly changing this market.
- 80% of enterprises will complement on-premise analytics and BI systems with SaaS-based applications by 2014 according to Forrester. From the persona study I am working on, the cut-over is going to be much more abrupt. 50% or less of companies will most likely have on-premise applications in place by 2014, as SaaS-based analytics and BI applications provide business users with the information they need when they need it.
- iPads running analytics and BI apps with lenders’ data in real-time are the new bling in financial services. One CIO who recently was recruited to a financial services firm told me he quickly picked out the other C-level execs in the room – they all had iPads running analytics apps with live customer data. One of his first projects: make that happen for the sales teams with a policy app. Everyone I’ve spoken with on this research study tells me being able to get to their analytics data on an Android or Apple iOS device is critical for their build-out plans.
- The greatest concerns the CIOs continue to have with SaaS are data integration to legacy systems and lack of security standards. Despite these concerns one CIO summed it up well and said “We really don’t have a choice, the legacy reporting apps are too high maintenance, don’t integrate to our new workflows well – we need a new reporting platform, so we are piloting a SaaS-based analytics app right now.”
- The high cost of maintaining legacy reporting applications, integrating them to the latest Microsoft, Oracle or SAP databases, and preserving tribal knowledge are factors pushing CIOs to adopt SaaS-based analytics and BI apps. One of the CIOs is with a government subcontractor who has Airbus, Boeing, Sikrosky Aircraft as their largest clients explained how dashboards are manually generated in Excel, taking weeks and often being inaccurate. To comply with contracts they must move to a faster reporting process. Using SaaS-based analytics in pilots have trimmed the time for creating dashboards from weeks to hours.
- Cloud integration and security are the skill sets these CIOs are hiring for right now. A quick analysis from Google Insights shows the rapid ascent of cloud integration as a search term. While Insights doesn’t provide demographics, the persona interviews underscore this trend.
- Analytics and BI data integration wins are setting the foundation for more complex system migrations in the future. Bank of America, Citibank and other financial institutions have client-side systems that are outpacing legacy systems’ ability to analyze and make use of the massive amount of inbound data they provide. Implementing cloud integration projects successfully, in conjunction the successful launch of more scalable SaaS-based analytics and BI applications sets the foundation for migrating even more complex systems. The Forrester Report Understanding The Business Intelligence Growth Opportunity included the following graphic with further underscores this point.
Bottom line: The lessons learned from migrating analytics and reporting from legacy to SaaS-based analytics and BI applications, combined with the need to have customer and market intelligence on mobile devices, is leading to rapid changes in this market.