Date posted: November 24, 2015

A recent post on manufacturing.net points to business process management (BPM) becoming top-of-mind for IT executives. Citing a recent survey by UK-based software company PNMsoft, the article notes “Most IT executives believe that business process management software can create significant revenue and savings increases.”

An article on tech.co underscores the point:

Business processes define how a company operates and stands out from competition. Globalization, increased competition, and the need to adapt to stricter regulations require efficient process optimization in order to achieve operational excellence. Competitive advantage lies in the ability to adapt the business processes to the always-changing conditions in a quick and efficient manner.

This leads some business professionals to go into areas they do not fully understand, like learning how to leverage IT platforms to better manage business processes. When tackling this problem, Business Process Management should be used: a management discipline and a set of technologies that provides support to the process management.

Business process management focuses on improving corporate performance by managing and optimizing a company’s business processes. It can therefore be described as a process optimization process. BPM enables organizations to be more efficient, more effective and more capable of change than a functionally focused, traditional hierarchical management approach. These processes can impact the cost and revenue generation of an organization. As a policy-making approach, BPM sees processes as important assets of an organization that must be understood, managed, and developed to announce value-added products and services to clients or customers.

The BPM sector is growing significantly, with expectations of a global $10 billion value by 2020, according to a study by Winter Green Research. While BPM software vendors initially focused on the technological implementation layer, today they are providing BPM services with a full range of features and functions.

“It is evident through these emerging trends that IT leaders understand business process optimization is vital to a business’ ability to survive and thrive in today’s digital landscape,” says PNMsoft Chief Technology Officer James Luxford.

The sector’s strong and ongoing growth supports that conclusion.

Date posted: November 19, 2015

A recent article on tdwi.org suggests that “big” may be too small a word to describe what is happening with data, as a trio of factors are combining to accelerate the exponential growth of the volume of data that enterprises must contend with: the Internet of Things (IoT); social media; and improvements in database management technology.

• The Internet of Things
  Sensors, meters, biochips, transponders, controllers, appliances, wearables, etc., will constitute 50 billion devices by 2020 that will generate 50 to 100 trillion objects of data. Just a decade ago a terabyte was considered a lot of data, but now we routinely talk about analyzing petabytes of data, which is 10 to the 15th power or 1000 terabytes. Tomorrow we will be talking about exabytes of data, which is 10 to the 18th power or one million terabytes.

• Social media
  Interactive marketing spend is growing at 17 percent per year and social media spend is growing at 34 percent. Last year, companies spent $55 billion on social media ads… 6.4 quintillion bytes of Internet data were created in 2014 alone; 90 percent of all Internet data was generated in the last two years, and 43 percent of data created on personal social media accounts is gathered and analyzed. In general, structured and unstructured data is growing 60 percent annually, and as third-world countries become more connected, don’t expect that to slow down any time soon.

• Improvements in Database Management Technology
  The relational database hasn’t changed much since it was invented in 1970. Most investment and innovation by vendors was been incremental, and the brand new models never lived up to the success of the relational model. Then document-based databases showed up about eight years ago and standards such as Javascript Object Notation (JSON) became accepted standards. Suddenly companies such as MongoDB and CouchDB were off to the races, adopted by independent software companies and large companies with Java development staffs. They introduced horizontal scaling and large file storage and processing. These products did not rely on SQL, but rather let the application itself define the queries. NoSQL is not where all the new innovation is— SQL database companies such as IBM, Microsoft, and Oracle picked up the pace to accommodate large transactional database tables and newcomers such as NuoDB invented scale-out distributed SQL databases for cloud deployment. This start-up claims it can do 1,000,000 new order transactions per minute.

Individually, any one of these factors would accelerate the pace of data growth, but collectively they are driving its explosion. As Eric Schmidt of Google reminded us at the decade’s outset, “There were 5 exabytes of information created between the dawn of civilization through 2003; now that much information is created every two days.”

Date posted: November 17, 2015

Customer Relationship Management (CRM) software is all about optimizing customer interactions and informing every transaction with a customer’s information to make each experience helpful to customer strategy.    Business Intelligence (BI) tools are designed to give organizations direct access to all of the information they pull in, and to transform that raw data into digestible analytics and insights that business users can understand. Combining CRM and BI can help a business become a smarter business.

A recent article on PC Magazine gives nine tips for ensuring the integration of CRM and BI platforms, and capitalizing on the deep data synergy the combination enables:

1. Understand CRM operations
   A company can’t begin to use a BI tool to gain customer insights if it doesn’t have an accurate picture of everything being doing right and how it can improve on the customer support representative side. A business should ask itself certain questions, including:

• Are our employees following best practices and using all available CRM tools during interactions?
• Is our customer support team properly staffed?
• Are there any unnecessary steps or roadblocks in the customer exchange?
• Do higher value customers receive a different experience?

Starting with these questions will give the business a better understanding of the customer experience before layering BI on top.

2. Understand customers
   BI tools work best when a business already knows what information it’s looking for. Do you know what’s the extent of the information your current CRM solution provides? After implementing the BI solution, do you know what the key metrics are that you’re interested in finding? Today’s customers, especially Millennials, generally offer businesses a wealth of personal information, much of it publicly available on various social media platforms. Consider using a Social CRM strategy to find out what you know and what you don’t know about your customer base.
3. Plan intelligently
   Don’t jump into a BI solution without a plan for how it fits into not only your CRM strategy but into all of your company’s infrastructure and services.
4. Select the right BI tool
   Take careful consideration when choosing the BI solution that’s the right fit for your business. Make sure the BI tool integrates with your existing CRM software, be it through a front-facing integration or via an application programming interface (API).
5. Do some data triage
   A BI tool will inundate an enterprise with mountains of data if you let it. Examining your existing operations and customers, and then intelligently planning around both should have already given you an idea of your business objectives. But, once the BI tool begins populating, you’ll need to make the Big Data a lot smaller by exporting a small number of metrics at a time to the tool’s various charting and data visualization features. To start, much of this data will also be unstructured— so be sure to sample and test each data source to eliminate redundancy and to ensure only accurate, consolidated data makes it to data warehouse storage.
6. Make the hard choices
   A BI tool identifies the strengths and weaknesses in every type of customer interaction, and graphically displays both in a chart or report. Business managers then have to decide what to do with the data. That may mean reallocating resources, reorganizing workflows, or completely overhauling a process or customer interaction procedure.
7. Be a data scientist
   With every customer interaction and business transaction now informed by Big Data and interpreted through your BI tool, it’s time to experiment and test. Implement minor or major changes to your customer interactions (or any other aspect of the business) and track the results on customer satisfaction, productivity, profits, or any other viable metric as if you’re conducting a controlled experiment. The same idea applies when rolling out a new product or service: evaluate its progress and profitability with BI data at each phase.
8. Streamline customer targeting
   Tying CRM and BI together will provide a much richer profile of every customer—from what platforms and tools they’re using in combination with your product to whether they prefer online or in-store interactions. On top of Social CRM data, this wealth of customer insights should be directly applied to reorganizing and segmenting your customer base. The more well-defined categories and sections a business delineates, the more effectively its marketing and sales staff can craft an effective strategy to convert, maintain, and monetize customer relationships.
9. Be smarter
   Once you’ve truly integrated CRM and BI, there is no limit to how the interlocking services can facilitate innovation. Giving customer support staff access to BI analytics encourages more personal transactions. Real-time BI data can be tied to alerts to improve CRM response time. Leveraging BI data for predictive modeling and analysis can help anticipate customer problems before they occur. If a business has planned out its CRM/BI integration and gone about the transition with a clear plan in mind, the results on the overall customer experience and, ultimately, on an enterprise’s bottom line can be dramatic and long lasting.

Date posted: November 12, 2015

A recent McKinsey Insight discusses the issue of recruiting and retaining more women in technology organizations, featuring interviews with board members of the nonprofit organization Girls Who Code. The publication notes:

Gender diversity remains an issue in technology organizations. The number of young women completing engineering and technology programs has dropped significantly over the past 30 years, and a report from the National Center for Women & Information Technology suggests that a little more than half of all US women who do enter technology fields leave their employers midcareer.

The leaders of Girls Who Code indicate that solving the gender-diversity problem has a two-part answer:

1. “Normalizing” technology as a career path for women.
2. Corporate support once women who have chosen a STEM (Science, Technology, Engineering, Math) career path have graduated university and formally entered industry.

Excerpts and videos of the interviews are included in the Insight, where Jamie Miller, Senior Vice President and CIO at GE; Alexis Maybank, Founder and Chief Strategy Officer at Gilt Groupe; and Jane Chwick, retired Partner and Co-COO of Technology at Goldman-Sachs shed some welcome light on this important topic.

I highly recommend you avail yourself of this piece, which you can access here.

Date posted: November 10, 2015

A recent post on CIO takes on the build versus buy question in enterprise software, framing the issue quite clearly:

A rational build vs. buy decision starts with well-defined requirements. Then potential products are evaluated to measure how well they meet those requirements. If you are considering replacing a homegrown product, include that in the evaluation. It is usually worth starting a new enterprise software project with an evaluation, even if the ultimate result is a development project. So how can the build vs. buy question be answered in a rational way without getting overwhelmed by all the work?

Part of what drives the impulse to build software is the idea that all requirements can be met through the effort. This, the author notes, is a mirage. “Resource constraints mean coding must be prioritized, and some requirements will never be met. Then the team may not fully understand the problem domain, and may not discover unknown requirements.”

A good starting point is using a tool designed for software evaluation and selection. To discover all requirements, reverse engineer features back into requirements: start with the homegrown code to develop a baseline, and then reverse engineer potential replacement products. This effectively builds a list of requirements that includes the unknowns. Importantly, front-loading requirements development drives down project risk.

Once a requirements list is established, requirements must be rated for importance. This not only helps build stakeholder buy in, but also ensures adequate capture of organizational needs.

Doing the math

 The requirements should be leveraged to create the RFI/RFP sent to potential vendors. Responses should be scored in software evaluation and selection application, which will automatically do a gap analysis and calculate fit scores. Then the organization can move to the build or buy decision:

Once you have fit scores for the homegrown product and potential replacements, you can rationally answer the build or buy question. Assuming normalized fit scores where 100% means all requirements are fully met, if several cloud or off-the-shelf products have a fit score of 80 percent or more, then buying is the right way to go.

If all commercial products score lower than 60%, there are three other possibilities:

• Reduce the scope of the project by eliminating certain functionality.
• Combining one product with a small custom code module
• Combining two or more commercial products

Each of the above increases the fit score. If none of these approaches works well enough, then building the app can be the right way to go.

If an enterprise has an in-house development team, there will always be a push to build. But it is usually cheaper and faster to buy than to build, and as the author notes, “If a problem has been adequately solved in a commercial product, why solve it again?”