Covering Disruptive Technology Powering Business in The Digital Age

Home > DTA news > Blog > Avoid Ingesting Dirty Data!
Avoid Ingesting Dirty Data!
August 17, 2020 Blog

 

Is your enterprise paddling in the shallow waters of a data lake or a data swamp? For businesses, both can be a possible scenario, but the latter is worse since data could not be fully utilised and sometimes left unused forever. In a data lake, both structured and unstructured data is stored in a centralised repository and data experts can still gain insights from this by applying various techniques.

If left unsupervised, however, data lakes could turn into data swamps where disparate data is stuck and has little to no organisation, leaving it useless for the companies. Data swamps often include irrelevant data which lack governance and metadata. These are important so that data experts can search for important data easily that will create an advantage to businesses.

Especially in this era of digital transformation, this data could hold very valuable insights, if fed into AI to truly extract hidden benefits from within. Nevertheless, enterprises often fail to reach this stage of innovation because of improper and incorrect collection of data in the first place.

IBM states that 81 per cent of business leaders do not understand the data and infrastructure required for AI. Without that fundamental understanding, it would be impossible for businesses to unlock the true potential data regardless of how advanced their AI algorithms may be.

Information architecture is the foundation on which data is organised and structured across a company. With a unified, prescriptive, information architecture, organisations can modernise their data architecture to make their data ready for an AI and multicloud world.

According to IBM experts, while business leaders do list improving data as a top priority, only 15% of them are actually making the most their data. The problem is that up to 80% of their data is locked up in silos, or not in a business-ready format. In other words, enterprises are sitting on a wealth of data – but most are not getting the value out of it.

However, companies need to know first what type of data they are digesting to build appropriate information architecture. They need to avoid data swamps so that data can be maximised and used efficiently.

This could be achieved by avoiding the ingesting of dirty data, or data that is erroneous, incomplete, inaccurate, non-integrated, duplicated or those that may violate regulation rules. For profiling and organising quality data, companies will need a platform. IBM Cloud Pak for Data allows you to do this by fully integrating data and AI platforms that modernise how businesses collect, organise and analyse data to infuse AI throughout their organisations.

IBM DataStage on IBM Cloud Pak for Data also provides real-time delivery of trusted data into data lakes, data warehouses or any other multi or hybrid cloud environment to feed business-ready data into AI applications. Some of the features of the platform include:

  • Profile, cleanse, integrate and catalogue all types of data.
  • Manage fluid data with protection and compliance (e.g., GDPR).
  • Policy and business-driven visibility, discovery and reporting.
  • Govern data lakes and data warehouse offloading.
  • Persona-based experiences with built-in industry models.
  • Embedded machine learning automation.

The appropriate digestion of data is only one of the steps in modernisation through AI, but it is the most critical part since enterprises will only move forward in innovations and advancements when they have the right data and the valuable insights it could provide.

Implementing new technology into an already complex IT ecosystem isn’t always easy. For companies who opt for IBM’s products and services to their businesses and require assistance in implementing such technologies, experts from IBM business intelligence partner BPI Technologies has the experience to help you, from data management to data analytics. For further information, click here.

(0)(0)