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Tag: business intelligence

  • 5 Ways KPI Analysis Helps DevOps

    5 Ways KPI Analysis Helps DevOps

    Four Vs drive big data solutions: volume, velocity, variety and veracity. Volume and velocity are technical considerations usually receiving a healthy dose of attention among architects and coders. However, variety and veracity often determine success or failure, and they tend to sneak up on teams who haven’t fully considered them. Key performance indicator, or KPI, analysis can head off variety and veracity problems before they escalate.

    When some folks say “KPI analysis,” they mean KPI monitoring and evaluation—the business intelligence perspective. I’m talking about a DevOps analysis discovering which KPIs should be monitored and what data is needed. One step in this analysis traces each desired KPI back through necessary transforms to its required data sources, often termed as facts. Such an analysis clarifies variety and quickly establishes a path to veracity.

    From a strategy perspective, KPI analysis can also mitigate the symptoms of “big bad data” that devour an organization, consuming people and resources rapidly. Here are five ways KPI analysis helps DevOps teams get a handle on big data.

    1. It prevents over-measuring. If you don’t know what you’re looking for, more data probably won’t help you find it. Teams experiencing FOMO (fear of missing out) expand their data gathering, hoping more variety uncovers something interesting. Once your KPIs have been discovered and fully analyzed, stop exploring and start exploiting. Stick to monitoring KPIs directly driving the most impact for your business.
    2. It minimizes bloating. In any computational system, the most expensive operation is moving data. Simply moving stuff around eats processor, network and storage bandwidth. Even with cost-effective storage available, unnecessary data means a bloated infrastructure and less efficiency. KPI analysis identifies a minimum spanning set of data sources. A workload-optimized solution then concentrates on efficient algorithm implementations, yielding results aligned with your KPIs.
    3. It stops wallowing. Allowing people too close to raw big data is extremely dangerous. Off-the-shelf analytics tools often carry a lot of non-KPI variables with them. Cloud-based martech tools are some of the biggest offenders; they are essentially showing off how many ways they can expose their underlying data. Avoiding the temptation to wallow through intriguing but ultimately irrelevant data saves precious brain cells. Customize reports and optimize dashboards around your KPIs.
    4. It de-obfuscates conversations. Nope, not talking about JavaScript here. We want everyone in the organization talking about the same KPIs and only those KPIs. You’ve seen those presentations where someone tries to dazzle an audience with mostly meaningless charts and graphs. Obfuscating your KPIs underneath a pile of other observations wastes time and risks defeating the purpose. Trust your KPIs and focus all organizational conversation on those.
    5. It thwarts non-causality. This is one place where data scientists and analysts really earn their money. Part of a solid KPI analysis determines causality—if your data sources change by some amount, the KPI needle responds by a quantified amount. Understanding those relationships is critical in avoiding false conclusions from data. If your KPI isn’t moving despite changes at the data sources, or your KPI is changing and the obvious data sources aren’t, step back and look at causality and transforms again.

    If you’re seeing these symptoms developing in your organization, it might be a sign automation was rushed in before KPIs were completely analyzed. At that point the toughest symptom to deal with is bloating. You may be stuck with a sized and funded infrastructure, and you really shouldn’t throw out good data already captured. Ongoing KPI analysis can lead your team to optimizations moving forward.

    Ideally, KPI analysis is performed upfront and these symptoms never develop. Realistically, complex systems have cycles of learning before their behavior is fully understood. Even a good upfront analysis may benefit from a revisit as trends start becoming apparent. When KPIs are rolled up periodically, you’ll need a minimum of six periods of results before expected causality is confirmed, maybe more. If that period is monthly, you’ll need ample patience.

    You’re spending too much on a big data solution to afford a big bad data outcome. KPI analysis is an important step, one DevOps teams with both technical and business expertise can excel at with some practice.

    — Don Dingee

  • Survey: CIOs Driving Digital Transformation

    Survey: CIOs Driving Digital Transformation

    A survey from IT World Canada (ITWC) apparently shows that CIOs are preferred by their companies to shepherd the digital transformation process in part because of their knowledge of legacy systems. The fifth annual CanadianCIO Census concludes that CIOs are “able to bridge in-place technologies required to run the business, while increasingly guiding their companies on how to grow and transform the business using digital technologies.”

    Going hand in hand with that notion is the survey’s finding that the rise of the chief digital officer is a myth.

    There are other interesting takeaways from the survey, which was completed by 164 IT leaders, mostly CIOs, in April:

    The 2017-2018 time frame is shaping up to be a growth year with nearly half of respondents expecting to add IT staff this year, and only 7 percent expecting to decrease staff. Increased IT budgets are also the trend for 2018, with 57 percent of the respondents reporting that they expect an increased IT budget. Some 11 percent expect an increase of more than 15 percent.

    Although the No. 1 concern of respondents is data security/data privacy, the more significant bit of data may be that the third-highest concern is business innovation, which rose significantly in priority over last year’s survey. Canadian CIOs also seem less enthralled with some of the latest technologies that some of their U.S. counterparts ballyhoo. Social media, artificial intelligence, blockchain and even internet of things rated lowest in terms of “benefits realized to date from investment” in these technologies.

    The picture that emerges from the survey is a strong tension between opposing forces faced by IT executives. Cloud services, though heavily embraced and widely successful, still haven’t brought down the price of keeping the lights on sufficiently to divert enough IT budget to digital transformation and business innovation projects. The transition of funding toward digital business is inevitable in some companies, and senior IT leaders need to avoid being run over by that freight train. If IT budgets grow year over year, investment in digital transformation should be stepped up significantly, too. Find ways to automate processes that were more labor-intensive before.

    Analysis

    Although the survey finds that the CIO is the de facto digital transformation technology leader, I’m not sure you can draw that as a hard conclusion from a survey of 164 mostly CIOs. CIOs are a logical choice for this role, assuming they possess strong technical acumen, in-depth knowledge of the systems in place and up-to-date insights on the latest tools and services at their disposal. Many CIOs have shrewdly focused on the business innovation end of things—in other words, what you can do with a digitally transformed enterprise. And in the long run, that may be the smarter position for a CIO to take.

    No CIO should assume they are in the driver seat on digital transformation. On the other hand, grooming and selecting a lieutenant to execute on the digital plumbing while you plan and implement your first digital business wins may be the better approach for some CIOs.

    — Scot Finnie