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Data divinity

How we get to the bottom of your data lake

We start by considering if your business goals are aligned with your data and analytics strategy.
In order to achieve this, we use a Data Analytics and AI maturity evaluation model
that consists of the following steps:

A successful data strategy involves partnering across business lines to identify data driven solutions for business challenges. 

Another important aspect is the creation of a comprehensive plan that covers the collection, ingestion, storage, and processing of data. The plan should focus on preparing the data for the development of new data products and services.

As the saying “garbage in, garbage out” goes, data quality and governance should play a critical role in the data strategy. 

Becoming data driven also includes organizational aspects such as developing a data literacy culture across the whole organization. Consider the creation of a Data and Analytics Center of Excellence, and introduce business roles advocating for good data practices (Data Analysts, Data Stewards, Data Scientists, etc.).

Close collaboration between data experts and others in the business is essential in creating valuable data products that the business understands and appreciates. 

Finding the right methodology starts with defining a problem statement; what problem are we trying to solve? Also consider whether data analytics is the preferred method for solving the problem. Once this is clearly shaped and evaluated by stakeholders, the analytical approach is defined. E.g., How can we use existing data? Which other data helps solving the problem and how can we collect/create it? Which algorithm is best suited?

Besides this, it’s always important to follow solid guidelines for model/product development so that work doesn’t become redundant.

Promote data literacy within all levels of the organization to stimulate spotting opportunities and reduce (security) risks. The more employees are data aware, the better data can be used to advance the business. 

It is essential to control who has access to which data in the organization. Create policies stating who can have access, how to request access, and how access is managed and monitored. Define data owners and communicate this clearly to the rest of the organization.

Investigate whether the organization’s current data (analytics) tools are robust to changes, such as increasing data volumes and data users. Consider all tasks performed in these tools, e.g., data exploration, preparation, modeling, scoring, deployment, and collaboration.

When your business changes, so must the tools and platforms you use to reach your new goals. 

The data landscape is evolving at breakneck speed, so ensure your asset requirements fulfill current expectations and enable scaling in the future. The following questions can support the analysis and management of your assets: What has become obsolete? What defers innovation and fosters inertia? Are your current data assets scalable enough? What can be automated? Can non-technical users use data products without a solid programming background?

What tools, including open source, are used within the organization for data analytics objectives? Verify their usefulness and security policies, and find alternatives that better support working with data if needed. Other characteristics of tools that are becoming more important such as their impact on the environment can also be considered when looking for alternatives.

To unlock the full potential of data products, facilitate bringing them into production using skilled employees and supportive tools. This helps meeting business objectives in time and having control over the products in case of (data) irregularities.