Five Methods to Improve Your Strategy for Data Management 

The Benefits & Best Practices of Cloud Data Management 

In the modern business world, organisations need to work efficiently to succeed and outperform their competition. Central to this is how these organisations are able to manage their data, because when a company grows, more and more people need to be able access business data whilst also still maintaining data security.

What is an enterprise data warehouse?

“an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. (E)DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.” - wikipedia

One of the biggest problems with Enterprise Data Warehouses (EDWs) is that they are being used in ways that were not intended. This is increasing the cost of them for many organisations.

Some thoughts and learnings from the pandemic applied to data management.

Firstly, I hope everyone is safe and well. In these times and it makes you think and consider your work balance versus what you do daily and how it somehow relates to the work we all do professionally in data/information management 😊.

Tired of lengthy data integration processes in your day to day database activities?
With the IBM InfoSphere DataStage, data integration can be streamlined across multiple applications by utilising its multi-cloud platform.

Use case for Watson Catalog (part of the IBM Cloud Pak for Data)

Every organisation would like to leverage the power of data they collect to drive insights, to improve efficiency, to become more responsive, to apply machine learning and AI and stay ahead of competition.

Data catalogs form a critical tool in establishing a common glossary of data for data consumers, data creators and data curators. Data catalog provides this centralised glossary and organises enterprise data sets to help data users perform searches for specific data that they need and understand its meta data, such as data lineage, uses, and how others perceive the data’s value.

Using AI and Big Data has become the norm to get work done in day to day life. In the meantime, it's essential to keep in mind that AI and Big Data has given life to a range of new cybersecurity challenges as well. If you are planning to reap the benefits that come along with AI and Big Data, you need to have a basic understanding of these challenges. Having this understanding will ensure your businesses security and protection in the long run.

Get your data ready for AI with IBM Cloud Private for Data

At the Dot Group, we can’t stress enough about the importance of a robust information architecture in order to monetise your data. Artificial Intelligence enabled analytics can provide insights into customer behaviour, business operations and offer a humongous opportunity for business to provide personalised / exceptional customer service, reduce the cost of operations and create offerings to beat competition. However, let’s not forget that AI and analytics are only as good the quality of your data.

Every enterprise has a mix of software solutions to address the various challenges they experience in their data management lifecycle. Data governance, data protection, and security, native storage and hybrid cloud management, data cataloging , data accessibility, reporting – are just some of the data challenges data stewards deal with daily.

When you are working as a data analyst for a company, you will often find difficulties in getting your hands on data. When proper data is not available, it can also be challenging to analyse it on time. This is where you need to think about using a data catalog.