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OBSTACLES THAT COMPANIES FACE WITH THEIR DATA WAREHOUSE


There are three main things that your business needs in order to be great at working with data.

First thing is ready data for your people.

And the real insight comes when you connect all your data assets in scale. You should bring all these assets officially into your not only warehouse but also lakehouse architecture. But that is just a start, you also need flexibly combine, govern, and compliance standards and that is the first main thing.

The Second one is Empowerment. It is about how you empower everyone with different experiences in your organization to see, understand and analyze all your data. Today 20% of people using the data consistently, and 80- 90% of this 20% are the ones who built that data culture (only IT teams). The first group of People uses dashboards to combine data flexibly that that comes from different sources and build these kinds of guided with their experiences. The second group of people are the people that are asking brand new questions and set of using dashboards, they typically use slight natural language and look analytics. And the third group of people who have insights embedded within the application workflow. So I see it very important that to bring analytics embedded into all the applications where people use that is where your lakehouse gets better at consume.

The data point is not the end, in order to be more relevant, in order to make the business continues to use it, and need to be kicked off work from a data point more efficiently.


TRUST ALL YOUR DATA

Ensure data is reliable, up-to-date, and aligns with your security, governance, and compliance standards.

Governance of Data (Lineage and data certification / Row-level permissioning / Usage analytics) : We should make it easy for people to access all the data they need to make decisions about the business (especially marketing and sales teams). And also make them able to combine that asset in a new way. So when you publish your data assets in your lakehouse, you need to build recombine them to deliver revenue insights.

Data Preparation (Shape and Clean your data) and Data Access (Combine Data flexibly) : Data Preparation (Shape and Clean your data) : the data is not necessarily in the right shape for analysis. You can give your users tools to reshape data, they will typically extract the data out of the platform into excel and give them the use of control.

Finally, governance is making sure that when you build the trustful report on top of data asset, you know where data comes from and also where is being used.


ANALYTICAL EMPOWERMENT

Real practical measurements here, firstly make it easy for people to discover assets they work aware of. People tend to use a single set of assets and you need to provide recommendations and easy search so they can beak out of analytical side.

Secondly, there are three human being experiences that allow you to reach more users which are visual analytics, dashboards, and most important one natural language.

Collaboration (Data storytelling / Share and comment Subscriptions, metrics, data-driven alerts):

Your analytical Empowerment needs to make it easy for you to invite people into the discussion to subscribe people to metrics to data-driven alerts more obvious. That is the way you drive at engagement and you drive consumption of data.

Provide Insight Where People Work with Data. Three things you need to do here. You embedded framework to bring data into applications use mobile experiences and final and emerging trend is workflow integration that is where you drive engagement of data by making these people make it power they work for.


Dr. Ella Burcu Keskin

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