Digital Transformation Requires an End-to-End Data Management Platform

The world is quickly moving towards an era of greater digital technology and feasibility. Organizations are starting to define their AI strategies to benefit from the potential to increase the labor productivity by up to 40% and to enable employees to make more efficient use of their time. These changes have created the need for better infrastructure within organizations across the globe. 

Organizations now have to take the whole ecosystem into consideration when debating whether they are implementing the right decisions for survival. With technological changes dictating the move towards the future, it isn’t uncommon to see organizations that don’t work enough to implement the changes lagging behind. One very important reason why organizations do not fare well in this digital world is because their business models aren’t congruent with the features of the digital economy. A business model that is not based on digital transformation will not be able to lead the way for efficient and optimal market practices. Besides implementing changes on the front-end business model, organizations also need to ensure that they revamp their systems at the back-end, and monitor the way that they work. 

Organizations need to implement the changes at every level of the management process, and should focus on getting a platform that manages data of all sorts and sizes. Thus, end-to-end data management is necessary for all organizations across the world, regardless of their stature, size, and area of expertise. 

A data management system that is elaborate, comprehensive, and provides solutions for integration, is more of a need than a want in the current business setting. 

Why You Need an End-to-End Data Management Platform 

As a member of the IBM Watson Data Council Program, I was given the opportunity to test and explore IBM Watson Studio. The major reasons why companies need an end-to-end data management program to embrace digital transformation are:

• The data infrastructure most companies currently have in place is flawed and inefficient. A data infrastructure needs to limit all wastages, and should work efficiently over all data sources and all departments to make sure that the insights garnered through data are both trustworthy and reputable. 
• The big data that is being generated by most companies isn’t worthy of being classified as good data. The data does not have much value, and the end result from the insights is often ineffective. The data insights you have will only be as good as the data that is being entered in the first instance. So, if you want your machine learning to be valuable, then remember to put in quality data. 
• Data scientists who work in point solutions that often fail to integrate within the existing operation, and also lead to major repercussions down the line are a very important reason why you should get an end-to-end data management program—because it flawlessly migrates proof of concepts into production.

Having talked about the inefficiencies that basically result from poor data management, we will now discuss how you can benefit from an end-to-end data management program. It will not only be of assistance in removing the inefficiencies currently associated with data, but will also make way for future progress and application of artificial intelligence (AI) solutions. Some of the ways you can benefit are: 

• Organizations will be empowered to derive value from their own data. Raw data is not often valued that much, but data that is prepared for analytics and AI in the same end-to-end system will lead to better outputs and insights. 
• The implementation will ensure the smooth testing and delivery of data-driven applications that improve everything from the customer experience to internal workflows. 
• The process of an idea converting to delivery is relatively fast, with relatively less inefficiency. The management of data within organizations is often plagued by communicational and management inefficiencies that make the data unworthy of being considered valuable. These inefficiencies also mean that the process takes far longer than it probably should, and that there isn’t much being done to limit these inefficiencies within the organization. 
• Break down the difference in goals of each department and bring together all departments under one roof. All departments, team members, business analysts, and app developers should work towards the same aim with respect to data-driven initiatives, without any possible divergence. 
• There should be strict rules and regulations around governed data and restricting people from accessing it, e.g., by masking specific columns in an existing data set. By making data governance part of the data workflow, data can be more easily shared with those who have been granted access.

Source: IBM: masking data columns to hide sensitive data

What Does End-to-End Data Management Mean? 

An end-to-end data management process encompasses the full spectrum of the lifecycle behind data across multiple storage types and tiers. The management is comprehensive and incorporates all the known systems together. 

• Discover: This aspect of a data management program deals with the intelligent discovery of data assets that provide context to the case at hand. 
• Catalog: A rich index of all the data, so that it is easy for users to find data for use in analytics and AI projects. A portal for all your enterprise data, fully governed from the corporate level. 

Data Management Process 

The data management process relates to all that happens within Watson Studio from start to end, with an overview of all key tasks.

I experienced the whole process, which was quite comprehensive and is discussed below. 

• Connect and Access Data: The first part of the process in the platform is to connect and access all data points for further work on them. 
• Search and Find Relevant Data: The second touch point is searching and finding data relevant to you. This is a user-friendly and important aspect within the system. Watson Studio recommends data and analytics assets, and provides an overview of highly rated and recently added data sets next to the overview of search results and filters.

Source: IBM: All relevant sources in one overview

• Prepare Data: The next part of the process that I explored in Watson Studio was preparing data – ingesting, curating, cleaning, and enriching. 
• Build and Train AI and ML Models: The AI model in use defines the value of the data insights and how purposeful the data can be for you and your climb towards digital innovation. 

Source: IBM: Easily create and deploy Machine learning models

Deploy AI and ML Models: After these models were built, the next stage was their deployment and implementation. This stage is particularly easy to manage with Watson Studio and Watson Machine Learning, since the interface is equipped with numerous tools to assist you in the deployment of the models. The interface has interactive tools, especially one for collaboration, which can help you organize your data and management needs. 

Monitor, Analyze, and Manage: The next stage of the process is to monitor, analyze, and manage the whole process to check for any anomalies. Anomaly detection is important, and is an imperative part of the setup. 

Collaboration and Community: You can permit varying levels of access and govern the data within your team. Community gives you access to public data assets.

Based on what I saw on the Watson Studio interface, I feel that end-to-end data management is definitely the route to create better AI applications, and to improve the business models you currently have in place.  

This post was brought to you by IBM Watson Studio. I received compensation to write this post but all opinions expressed are my own.