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Top 5 Data Management Practices to Consider

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Top 5 Data Management Practices to Consider

We have ignored the power of data for too long. Let’s democratize data and listen to what it has to say. Learn five management practices that organizations need to adopt.

· Big Data Zone
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Agencies are struggling to differentiate their offerings, and although there is a lot of talk and investments being made in data, there is little guidance available. Here are five data management practices that agencies should consider adopting in the near term to stay relevant and differentiate themselves!

1. Own and Control Your Data

For a long time, we have relied on point-to-point integrations between systems. As long as we have our web analytics connected to our search and fed into our CRM, we complacently believe that all is well — except when that curious marketing analyst from the brand’s team poses an out-of-the-box question. It then becomes a full-blown project for your internal teams and after spending weeks extracting data out of various platforms, you are presented an incomplete view in a messy Excel sheet. Owning and managing your brand’s data will enable you to build deeper trust with brands by making their data visible — when they want it and how they want it. This will also allow your data science teams to continuously mine for new insights and deliver added value to clients.

2. Capture Data at the Lowest Level of Granularity

It is important to have aggregate metrics, summarized reports, and dashboards. But the power of raw data is limitless and hugely untapped. Capturing every single user interaction across as many touch points as possible allows for exploratory analysis such as building custom attribution models, overlap matrices, analyzing trends, identifying patterns, and applying machine learning and AI.

3. Outsource Integrations

There is no denying that integrations are extremely time-consuming to build and incredibly painful to maintain. Data formats and APIs continuously evolve, and it takes a whole team of engineers to keep up with this. There are a lot of companies out there that specialize in data integration to help abstract the complexity away. Invest in a solution that provides a solid data collection framework with reliability and security built-in.

4. Build a Data Platform That Lasts

It is easy to fall into the trap of focusing only on short-term needs and pain points. And building a specific application or a canned dashboard that solves a short-term pain point will only take you so far. Focus on building an open data platform that allows you to keep pace with evolving and emerging needs.

5. Make Security a Priority

The security of your client’s data has to be a key priority. Build security into the data platform that you build and the solutions that you invest in. Ensure that data is always encrypted during transit, as well as at rest, and that the platform has all the necessary compliance to handle the different types of data.

We have ignored the power of data for too long. Let’s democratize data and listen to what it has to say.

Find the perfect platform for a scalable self-service model to manage Big Data workloads in the Cloud. Download the free O'Reilly eBook to learn more.

Topics:
data science ,data management ,big data ,data analytics

Published at DZone with permission of Lakshmi Raman, DZone MVB. See the original article here.

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