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Ways in which Big Data Projects Can Go Wrong
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Ways in which Big Data Projects Can Go Wrong

You deployed your big data project, but it is not panning out the way you want it to. It’s not the first time this has happened to someone.

There have been several instances when big data platforms have struggled to deliver the desired outcomes in the last 10 years. Specific patterns of data failures have emerged over time which can be overcome with simple tweaks.

Let’s take a look at the most common ways in which big data projects can go wrong.

1. Overstuffing the Data Lake

Many business enterprises have raised concerns over not getting the most out of their data investments.

Even if you are using sophisticated tools like the Incorta Data Analytics Platformthe mere lack of data centralisation can limit the ability of the business to extract lasting value out of the platform. This is largely due to the fact that business enterprises tend to spread out their data in silos. This includes individual databases, applications, and file systems.

To overcome this, the solution was to put everything into data lakes instead. Overstuffing into data lakes led to a new set of problems. Too much data in a single place resulted in data inconsistencies, difficulty in management and lack of data purity.

The way is to take on the lakehouse architecture approach that can help to overcome the natural divisions among separate data sets. Moreover, it offers the manageability, governance, and quality of an SAP data warehouse.

2. Not Taking a Centralized View into Data

Centralising data into data lakes has been a major struggle for business enterprises. As a result, many companies have opted to proceed with looking at data in silos. This changes the end goal where enterprises now have to overcome as many barriers as possible that impede user access.

The overall data democratization effort is to ensure that the data is available to the authorised users. Centralising the data catalogs helps to provide an overview of disparate data assets whilst being able to monitor and keep track of access rights and governance.

Developing data catalogs comes with add-on benefits like enabling users to create data pipelines. This helps to interconnect with subsidiary systems instead of banking on third-party products to string them together.

Integrating this with the likes of a Unified Data Analytics Platform allows users to innovate and create great products for their target audience.

3. Going Too Big Too Fast

In the days of the Hadoop, a number of companies heavily invested in building massive clusters to power their cost-efficient data lakes that replaced data warehouses. As the trend is now towards the cloud era of data warehousing and data lake offerings, these same companies are holding back due to the need for upfront investment.

In reality, companies can even start with a small investment to leverage a cloud infrastructure like the SAP Analytics Cloud and gradually expand the system. In fact, it would be wise to start small with a single data project and monitor the results that it delivers instead of investing in a large-scale project.

Going forward, business enterprises can grow at a steady pace and start adding more cases as part of your cloud-based data lake or data warehouse.

In Conclusion

Business enterprises have come to heavily rely on data to make data driven business decisions. As systems become more technologically advanced, it becomes imperative to adapt to these innovative approaches to avert the risk of falling behind your competitors.

If you are a public sector business based in the Middle East looking to modernize your big data systems, then it’s time to speak to an expert from MDSap. A chosen SAP partner, MDSap is a trusted name offering a range of Sap Solutions for Public Sector companies in the MENA region.