Applai, Machine Learning, Artificiell Intelligence

10 reasons why your analytics program will fail

10 reasons (wide)

Analytics is a widely used term. How do you figure out which analytics is right for your organization? Just like most trips, an analytics destination is a good place to start. Even if it’s the most basic. You have to know where you want to go and what you want to see. And gather what’s needed to get you there.
If you want to talk more about this, let us know! You can easily book a free meeting with us here. But now to the main point, 10 reasons why your analytics program will fail.


1. The executive team doesn’t have a clear vision for its advanced-analytics programs 
(Usually derived from executive’s lacking understanding of advanced analytics and machine learning)


Solution: set up knowledge sharing workshops within the company to spread knowledge between technical and business employees

2. No one has determined the value that the initial use cases can deliver in the first year. (Leads to performing the wrong use cases.)

Solution: analyze business value and feasibility before starting building models and platforms

3. There’s no analytics strategy beyond a few use cases
Misses to capatilize on a broader level, such as broader business applications and collaborations with others in the eco system (read more about guidance towards 100% smartness here)
  • There are three crucial questions the CDO must ask the company’s business leaders:
    • What threats do technologies such as AI and advanced analytics pose for the company?
    • What are the opportunities to use such technologies to improve existing businesses?
    • How can we use data and analytics to create new opportunities?
4. Analytics roles—present and future—are poorly defined
  • Because of management’s lack of understanding of advanced analytics, the wrong competencies are hired, sub optimizing the work force and analytics initiatives

    Solution: Map and document exact capabilities needed today and years ahead, inventory existing ones and then hire the rest externally.
5. The organization lacks analytics translators
  • An often-overlooked role that translate business logic to data scientists and scales solutions across the organization. The role needs a mix of business knowledge, general technical fluency, and project-management excellence.
6. Analytics capabilities are isolated from the business, resulting in an ineffective analytics organization structure
Broadly categorized there are three ways to organize your analytics organization:
  • Centralized analytics function – Central unit owns all data and models, business units asks for analytic models. Risk for over-centralization creates bottlenecks and leads to a lack of business buy-in.

  • Decentralized analytics functions – Business units owns data and models. Decentralization brings the risk of different data models that don’t connect

  • Federated analytics function (Recommended) – Center of excellence lead by CDO is established and business units owns more or less of data and modelling, under more or less coordination from center of excellence
7. Costly data-cleansing efforts are started altogether
  • Business leaders tend to think that all available data should be scrubbed clean before analytics initiatives can begin. If so, up to 70% of data cleansing efforts are for nothing.
  • Solution: start by identifying and assessing use cases, start to build models and paralelly cleans the necessary data.
9. Analytics platforms aren’t built to purpose
  • More than half of all data lakes are not fit for purpose. Often, companies design the data lake as one entity, not understanding that it should be partitioned to address different types of use cases.
  1. Nobody knows the quantitative impact that analytics is providing
  • It is surprising how many companies are unable to attribute any bottom-line impact to these investments.
  • Solution: Business managers needs to set up a metric system and commit to measuring financial impact of use cases each quarter
10. No one is hyper-focused on identifying potential ethical, social, and regulatory implications of analytics initiatives
  • It is important to be able to anticipate how digital use cases will acquire and consume data and to understand whether there are any compromises to the regulatory requirements or any ethical issues.

*Based on McKinsey report and Applai experiences

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