Analytics concepts have been growing and maturing for a long time. Depending on how far back you want to look, William Playfair published the first business-oriented charts in 1785. Florence Nightingale produced ground-breaking medical cause-of-death charts in 1858. In the 20th century, analytics research provided profound insights into how to present data for maximum impact and understanding. In this century, analytics concepts are expressed in various powerful, productive software packages summarized at this link.
It wasn’t until more recently that the following trends coalesced so that analytics is becoming the new normal in many organizations:
- Dramatic reductions in the cost of computing infrastructure.
- Dramatic improvements in analytics and data warehouse software development productivity.
- Significant increases in the percentage of corporate data that is available digitally.
- Improvements in data quality.
- The rise of the data scientist profession.
- The recognition of the value of a data-driven decision-making process.
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Expect to see the number of analytics articles, webinars, conferences, software packages, and training offerings continue to grow. Build your expertise by spending some time exploring this rich analytics content.
Challenging the self-serve analytics model
Self-serve analytics has been a huge trend in recent years as end-users:
- Became frustrated with the slow, formal engagement model of their IS department.
- Experienced exciting analytics successes built with easy-to-use, high-productivity analytics development tools.
Self-serve analytics is beginning to encounter bumps on the road caused by end-users:
- Lacking knowledge of the availability and structure of corporate data stores.
- Missing the design input of experienced business analysts.
- Lacking the expertise to link internal and external data sources.
- Underappreciating the development complexity that must be addressed to create more valuable analytics.
Expect to see more end-users recognizing that the IS department can contribute to delivering valuable and robust analytics applications after all. You can accelerate this trend by channelling the cynicism of Business Intelligence (BI) practitioners.
Improving analytics collaboration
Business end-users are becoming less impatient, less critical and more understanding as they begin to appreciate that the analytics they’re asking for, while valuable, are not trivial to develop.
Data scientists recognize that the more arcane aspects of their profession may confuse executives more than helping to deliver value from the available data. They’re building more trust and credibility with more straightforward analytics.
IS staff is becoming less bureaucratic, less paranoid and more engaged as they begin to appreciate that they’re not being asked to create a production-quality application. Custom analytics do not need to be high availability, high throughput, and high security when requirements are changing at least daily.
Expect more collaboration among these disciplines to deliver sophisticated and valuable analytics applications. You can nurture this trend by facilitating a more mindful dialogue.
Growing end-user adoption of analytics
End-user interest in and adoption of analytics is growing due to:
- Continuing ease-of-use improvements in analytics development tools.
- More production-quality analytics applications becoming easily accessible in the corporate application portfolio.
- Strengthening of end-user support.
- More corporate and external data being integrated into the analytics environment.
- Improving data quality.
Expect to see most end-users acquire at least basic analytics literacy. You can encourage this adoption trend by strengthening best practices for data management and operating a center of analytics excellence.
Meandering through the analytics maturity model
- There’s more value in the later stages.
- You should build an ambitious plan that rockets your organization through the earlier stages as quickly as possible to the nirvana of the later stages.
- You’re an idiot if you don’t move beyond the earlier stages.
While there’s undoubtedly more value in the later stages, I believe:
- There’s lots of value in the earlier stages.
- You can waste a lot of money pushing an organization to the later stages faster than the organization can absorb.
- Vendors think you’ll spend more money on software licenses and services if they can encourage you or push you to move faster.
Expect to see organizations meandering through the maturity model stages at a more cautious, perhaps uneven pace to reduce the risk of over-investment and failure. You can reinforce this trend by addressing the impediments to analytic value.
Creating embedded analytics
Initially, analytics applications sat apart from enterprise applications even though they shared data and perhaps datastores. Analytics began this way because:
- The initial audiences for analytics were staff groups and not line-of-business departments.
- More insightful analytics are built by integrating data owned by multiple departments.
- Line-of-business departments are often immersed in running the business and not looking beyond immediate tactical issues.
More recently, it’s become apparent that embedding analytics into enterprise applications can deliver immediate value. Analytics can indeed help line-of-business departments improve their performance.
Expect to see organizations pursue both analytics trends in parallel. You can strengthen this trend by:
- Suggesting embedding analytics for corporate applications.
- Building out the analytics environment with more integrated data sources.
Yogi Schulz has over 40 years of information technology experience in various industries. Yogi works extensively in the petroleum industry. He manages projects that arise from changes in business requirements, the need to leverage technology opportunities, and mergers. His specialties include IT strategy, web strategy and project management.
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