You are currently browsing the category archive for the ‘Supply Chain Performance’ category.


Our latest benchmark research into the market for location analytics software finds significant demand for location-related technology that can improve business outcomes and generate VentanaResearch_LocationAnalyticsrelevant information for various types of users. (Location analytics is an extension of business analytics that can enhance the sophistication of data and processes by adding a geographic context.)  My last analyst perspective on this topic discussed the business value of insights based on geography and what organizations are doing to advance their efforts here. Our research also shows, however, that most still lack satisfaction and confidence in using the technology. Just 12 percent of all participants said they are very satisfied with the location information and analytics available in their organization. Further analysis shows that satisfaction increases with use of a dedicated application for location analytics: 71 percent of those are satisfied or very satisfied, substantially more than those using location analytics within a BI tool (22%); findings are similar for both B2B and B2C use. We find similar levels of confidence in the quality of location information: 15 percent of those using a dedicated application are very confident in their location analytics. Confidence in the reliability of such information is essential to more organizations adopting location analytics.

vr_LA_driving_change_in_location_analyticsOne cause of limited satisfaction and confidence appears to be the difficulty of analyzing information that has a location context. Two-thirds of organizations said doing so requires significant effort or some effort, and 17 percent said that is very difficult or they cannot do it. Thus it is not surprising that about three in fiveorganizations plan to change the way they use location information in the next 12 to 18 months. For more than 40 percent each, that change is driven by efforts to improve processes: a new initiative to improve information and decision-making (51%), a need to improve business-to-business planning and collaboration (50%), the desire to promote operational efficiency (49%) and as part of a wider analytics and business intelligence initiative (44%). Participants with IT titles most often identified as the driver a new initiative improving information and decision-making (61%), as did those from the services (69%) and government (63%) industry sectors; those working in lines of business insisted more on seeking change to improve B2B planning and collaboration (54%). The need for improvement shows that organizations recognize a potentially important role for location analytics in various business processes, from information use to decision-making.

A range of technologies can be used for location analytics, vr_LA_dedicated_technology_provides_satisfactionbut not all options work equally well. Today nearly half (49%) of organizations use spreadsheets heavily for analyzing information that includes location data; significantly fewer use other tools heavily – custom applications (36%), analytic or BI tools (34%) and a geographic information system (GIS, 23%). Many organizations use business applications heavily for analyzing this type of information, most often customer relationship management (CRM, 28%), supply chain management (16%) and enterprise asset management (14%) systems. Yet heavy users of a GIS or a dedicated application are the ones most often very satisfied (49%), and heavy users of spread­sheets are very satisfied least often (16%). Among those saying that the use of location analytics has im­proved their results, spreadsheet users ranked last (35%), far behind users of a GIS (55%) and analytic or BI tools (49%). Organizations that use a dedicated tool for location analytics (49%) are the most satisfied significantly more than those that use only spreadsheets (16%).

A look at the capabilities necessary for effective location analytics indicates why tools designed for the purpose get better results. More than three in five organizations said three basic capabilities are important: geographic representation of data, visual metrics associated with locations on a map, and selecting and analyzing locations on a map. One-half to one-third said interacting with maps and locations for further analysis, determining distance and drive time, and adding layers to maps are important. All of these basic capabilities are the building blocks for conducting specific analytics that can identify or recommend actions from the mashup of data about a location or to provide insights to guide decisions based on location-specific indicators.

Another technology approach used most frequently is business intelligence (BI). These tools are designed for reporting, creating dashboards and general access to analytic information such as metrics. BI tools and processes are established in both IT departments and lines of business, and location information can further enhance BI efforts. Nearly half (48%) of participants in this research ranked business intelligence interfaces as the most important to integrate with other enterprise software; custom interfaces was a distant second at only 13 percent. IT participants (55%) put BI first more often than did those in business (44%), and manufacturing (55%) ranked it higher than other industries. BI also is the application most often integrated with location analytics (45%), even more so in the largest companies by number of employees (56%) and by annual revenue (65%). In terms of planning and developing a strategy to use location analytics with other systems, most intend to integrate it with marketing automation (33%), sales force automation (30%) and enterprise content management (also 30%).

However, the research also finds impediments in using BI and location analytics together. Almost half  (46%) of participating organizations said that integrating the two requires significant effort; another 16 percent said doing that is very difficult and requires substantial time or that they have no practical way to do it. On a positive note, integration of these two technologies has advanced significantly in the last several years, and it is easier to exchange data and add it to presentations. In addition, organizations that use business intelligence to conduct location analytics reported benefits, particularly improving the customer experience (21%) and gaining competitive advantage (20%). More than three in five companies that use BI with location analytics are very satisfied (17%) or satisfied (44%) with theinformation and analytics they have available. Thus the research clearly shows that integrating location information into business intelligence can deliver value.

Looking at location information in a broader sense we find many organizations using consumer mapping to plot data quickly, predominantly free software such as from Google (which 45% use) and Microsoft (31%). The research also reveals that while almost one-third (31%) have used these for enterprise needs, only 8 percent are very satisfied with them. Like personal productivity tools, these tools can help in individual tasks like driving instructions and plotting locations for quick geographic placement, but they lack task support and operational or specific analytical context that requires secure, integrated access to enterprise systems. Free and easy access makes them attractive, but they do not provide enough capabilities for skilled workers to use in complex business tasks.

As deployments grow, so does the need to integrate and adapt location analytics to other technologies. For example, one in five research participants said mobile technology is critical for improving location analytics, as did smaller numbers for cloud computing (15%), big data (15%) and collaboration (8%). Ways of deploying location analytics also are changing, as more organizations realize that buying and installing the software on-premises (which 35% prefer) is not the only approach; nearly as many (33%) want to access it on demand through software as a service (SaaS). Very large companies by number of employees (44%) and annual revenue (39%) have the strongest bias for on-demand deployment, as does manufacturing (43%) among industry sectors. Exploiting the full potential of big data investments, whether representing machine data or customer locations, is a prime example of where location analytics can help use data effectively. The research strongly suggests that location analytics will have a place in evolving business technology environments and that broader use of innovative technology will extend the value of this investment also.

vr_LA_location_analytics_requires_experiencesHowever an organization deploys location analytics, the research shows that experience in using it is critical to success. Half of participating organizations have deployed location-focused technology, and the percentage is highest among very large companies by number of employees (56%) and annual revenue (67%). Almost two-thirds (62%) of all companies that have the most experience said location analytics has helped improve results significantly; among those who are somewhat experienced just 23 percent said this.

Organizations of course expect to realize important benefits from software investments. The top five benefits being sought from location analytics are to improve the customer experience and customer satisfaction; gain competitive advantage; improve access to and value of existing information; improve organizational alignment and coordination; and deliver products and services faster. Organizations that use a dedicated technology focus most on gaining competitive advantage (21%) and delivering products and services faster (16%). Investment in a dedicated tool for location analytics can increase the value of an organization’s information and analytics, which improves with experience in using the technology. We recommend that organizations develop a location-specific component in their agenda for analytics. If you want to learn more on the value and potential of technology in location analytics our community is available to help with more depth in best practices and insights on this topic.

Regards,

Mark Smith

CEO & Chief Research Officer


We recently released our benchmark research on big data analytics, and it sheds light on many of the most important discussions occurring in business technology today. The study’s structure was based on the big data analytics framework that I laid out last year as well as the framework that my colleague Mark Smith put forth on the four types of discovery technology available. These frameworks view big data and analytics as part of a major change that includes a movement from designed data to organic data, the bringing together of analytics and data in a single system, and a corresponding move away from the technology-oriented three Vs of big data to the business-oriented three Ws of data. Our big data analytics research confirms these trends but also reveals some important subtleties and new findings with respect to this important emerging market. I want to share three of the most interesting and even surprising results and their implications for the big data analytics market.

First, we note that communication and knowledge sharing is a primary vr_Big_Data_Analytics_06_benefits_realized_from_big_data_analyticsbenefit of big data analytics initiatives, but it is a latent one. Among organizations planning to deploy big data analytics, the benefits most often anticipated are faster response to opportunities and threats (57%), improving efficiency (57%), improving the customer experience (48%) and gaining competitive advantage (43%). However, once a big data analytics system has moved into production, the benefits most often mentioned as achieved are better communication and knowledge sharing (51%), gaining competitive advantage (51%), improved efficiency in business processes (49%) and improved customer experience and satisfaction (46%). (The chart shows rankings of first choices as most important.) Although the last three of these benefits are predictable, it’s noteworthy that the benefit of communication and knowledge sharing, while not a priority before deployment, becomes one of the two most often cited later.

As for the implications, in our view, one reason why communication and knowledge sharing are more often seen as a key benefit after deployment rather than before is that agreement on big data analytics terminology is often lacking within organizations. Participants from fewer than half (44%) of organizations said that the people making business technology decisions mostly agree or completely agree on the meaning of big data analytics, while the same number said there are many different opinions about its meaning. To address this particular challenge, companies should pay more attention to setting up internal communication structures prior to the launch of a big data analytics project, and we expect collaborative technologies to play a larger role in these initiatives going forward.

vr_Big_Data_Analytics_02_defining_big_data_analyticsA second finding of our research is that integration of distributed data is the most important enabler of big data analytics. Asked the meaning of big data analytics in terms of capabilities, the largest percentage (76%) of participants said it involves analyzing data from all sources rather than just one, while for 55 percent it means analyzing all of the data rather than just a sample of it. (We allowed multiple responses.) More than half (56%) told us they view big data as finding patterns in large and diverse data sets in Hadoop, which indicates the continuing influence of this original big data technology. A second tier of percentages emphasizes timeliness as an aspect of big data: doing real-time processing on streams of data (44%), visualizing large structured data sets in seconds (40%) and doing real-time scoring against a database record (36%).

The implications here are that the primary characteristic of big data analytics technology is the ability to analyze data from many data sources. This shows that companies today are focused on bringing together multiple information sources and secondarily being able to process all data rather than just a sample, as well as being able to do machine learning on especially large data sets. Fast processing and the ability to analyze streams of data are relegated to third position in these priorities. That suggests that the so-called three Vs of big data are confusing the discussion by prioritizing volume, velocity and variety all at once. For companies engaged in big data analytics today, sourcing and integration of various data sources in an expedient manner is the top priority, followed by the ideas of size and then speed of arrival of data.

Third, we found that usage is not relegated to particular industries, vr_Big_Data_Analytics_09_use_cases_for_big_data_analyticscertain types of companies or certain functional areas. From among 25 uses for big data analytics those that participants are personally involved with, three of the four most often mentioned involve customers and sales: enabling cross-selling and up-selling (38%), understanding the customer better (32%) and optimizing pricing (28%). Meanwhile, optimizing IT operations ranked fifth (24%) though it was most often chosen by those in IT roles (76%). What is particularly fascinating, however, is that 17 of the 25 use cases were named by more than 10 percent, which indicates many uses for big data analytics.

The primary implication of this finding is that big data analytics is not following the famous technology adoption curves outlined in books such as Geoffrey Moore’s seminal work, “Crossing the Chasm.” That is, companies are not following a narrowly defined path that solves only one particular problem. Instead, they are creatively deploying technological innovations en route to a diverse set of outcomes. And this is occurring across organizational functions and industries, including conservative ones, which conflicts with conventional wisdom. For this reason, companies are more often looking across industries and functional disciplines as part of their due diligence on big data analytics to come up with unique applications that may yield competitive advantage or organizational efficiencies.

In summary, it has been difficult for companies to define what big data analytics actually means and how to prioritize their investments accordingly. Research such as ours can help organizations address this issue. While the above discussion outlines a few of the interesting findings of this research, it also yields many more insights, related to aspects as diverse as big data in the cloud, sandbox environments, embedded predictive analytics, the most important data sources in use, and the challenges of choosing an architecture and deploying big data analytic products. For a copy of the executive summary download it directly from the Ventana Research community.

Regards,

Tony Cosentino

VP and Research Director

Enter your email address to follow this blog and receive notifications of new posts by email.

Join 119 other followers

Twitter Updates

Top Rated

Blog Stats

  • 65,964 hits
Follow

Get every new post delivered to your Inbox.

Join 119 other followers

%d bloggers like this: