Logicle Analytics, LLC

07/11/2022

"With transformation goals harder than ever to meet, businesses need to identify change agents across the organization."

For one, this means data is not an IT-only area of expertise. Siloed data is bad, but siloed cultures are even worse. As Kirshen notes, there's room for training and development here. One tool that can help? The LDIS+™ Analytics Impact Framework.

Data literacy is an essential business skill in the age of digital transformation. Build a data-driven culture with these tips.

07/06/2022

"The study revealed businesses struggling to harness data & analytics, with 64% of respondents quoting data across disparate systems and applications a key challenge to their strategies. Some 60% of executives also quoted a lack of data culture, along with legacy architecture (56%), a lack of data skills (53%), and a lack of ownership from business units (44%). "

This study points out cultural and managerial issues almost exclusively over technical ones. This is why the LDIS+™ Analytics Impact Framework exists. In the analytics sphere, there's so much noise and FOMO related to new tools and developments. What about culture? What about buy-in? If data were a new employee in the company, would it be greeted with an enabling culture?

Less than half of Australian data and analytics leaders are confident in their data strategy as siloes and lack of culture prevent innovation.

06/27/2022

Making sense of all the data in your business can be overwhelming, and you might need to go beyond the data visualizations and fancy charts to get those insights. Business intelligence is more than the tools that facilitate it and when done right can help you make accurate predictions and deploy data-driven decisions.

Listen to the full episode:

‎Show Research Labs Podcast by SundayPyjamas, Ep #25 Dr Jonathan Fowler on Data Strategy for Business - Jun 19, 2022

06/22/2022

More on data as a product...

The mindset change is perhaps the biggest hurdle to get over, even if the end users (internal customers) don't see any noticeable difference in what they get. This article describes a situation in which the end users were VERY aware of changes. Why?

Moving data engineering from the core data team to the domains. In a company where the business domains are dependent on the core data team, this is a major shift. In some cases, there are data-savvy stakeholders in the domains already, and are good candidates for domain-level data stewards.

These domain engineers were responsible for the data quality at their level. Organizations that are used to centralized data/IT teams, and dismissive of interoperability, won't be able to make this change.

Question: does such an obstacle, and reliance on data mesh, kill the possibility of data-as-a-product?

Our central P&T teams implemented data mesh architecture to manage and analyse the data our users generate.

06/20/2022

Of all the hype and trending words, "data as a product" stands out. The complex and inefficient model, presented in this article, is one that exists in many organizations and they aren't running around with their hair on fire. It works. But it might not be the best fit.

The data-as-a-product model simplifies consumption for even the most complex of organizations. The five consumption archetypes cover all varieties of needs and interactions, reducing bespoke pipelines to repeatable packages that can be customized with minimal effort. Overall, though, the data-as-a-product line of thinking emphasizes the delivery to, and support of, internal customers.

Creating reusable data products and patterns for piecing together data technologies enables companies to derive value from data today and tomorrow.

04/14/2022

How do you make less abstract and more impactful?

You meet everyone where they are, and you don't work with the least common denominator or highest-paid person's opinion (HIPPO).

The LDIS+™ Analytics Impact Framework begins with the grassroots and communicates to every single stakeholder that their perspective matters. It's not a top-down decree, it's a journey and an exercise in mastery. Knowing where the priorities are, how data is perceived across the organization, and what the biggest roadblocks are--even if the problem is that they don't know what they don't know--allow every initiative to start with the stakeholders at the forefront and strengthen a .

04/11/2022

With data analytics being the "source of truth and big lever for growth," why is it still being relegated to a simple deliverable instead of part of the fabric of the company?

We studied the phenomenon, developed the framework to address it, and have the training to solve it.

Startups have a clean slate when building out their analytics practice. Here’s what they’ve learned.

04/06/2022

Finishing our look at the four data culture archetypes in a series of posts...let's look at the Controlling set.

While every person and team has a blend of all four types, they tend to fall back on dominant archetypes when faced with pressure (e.g., digital transformation, transition, implementation, etc). The Controlling archetype values accountability and efficiency. Like the Creative set, they are especially averse to data siloes as they perpetuate chaos. Data management and governance are top priorities. Data is used to enforce compliance and regulate processes. These are your process thinkers; the ones who can take the whiteboard full of brain dumps and map out realistic workflows.

04/04/2022

Continuing our look at the four data culture archetypes in a series of posts...let's look at the Competitive set.

While every person and team has a blend of all four types, they tend to fall back on dominant archetypes when faced with pressure (e.g., digital transformation, transition, implementation, etc). The Competitive archetype values productivity and customer engagement. Access to data resources, adequate coverage of all users, and accuracy are all values. These are your drivers; the ones who will ask, "So what?"

03/30/2022

Continuing our look at the four data culture archetypes in a series of posts...let's look at the Creative set.

While every person and team has a blend of all four types, they tend to fall back on dominant archetypes when faced with pressure (e.g., digital transformation, transition, implementation, etc). The Creative archetype values innovation and adaptability. Data siloes are land mines...there must be clear ownership, management, and lineage for an integrated data environment. These are your innovators; the ones who understand your assets are your roadmap for new avenues of success while mitigating risk.

03/28/2022

We're going to look at the four data culture archetypes in a series of posts. First up: Collaborative.

While every person and team has a blend of all four types, they tend to fall back on dominant archetypes when faced with pressure (e.g., digital transformation, transition, implementation, etc). The Collaborative archetype values company commitment and participation. Data products must cover an adequate scope to make sure all are engaged. These are your mobilizers; the ones who understand your data culture is there to drive the company forward by building on the diverse strengths of all.

03/23/2022

Data-rich and information-poor. Next thing you know, here comes talk about digital transformation and some garden-variety AI solution.

Who's driving it? The project sponsor alone? The most vocal of employees who have complained enough? The group of colleagues who regularly interact with the project sponsor? The C-Suite? The budget? Each of those potential drivers has its own set of values and desired outcomes.

Before getting a new initiative off the ground, consider an assessment tool like the LDIS+™ to gain a deep understanding of priorities, values, challenges, needs, and shared goals across the organization.