Data Management Implementation

This is the transcript of a Webinar hosted by InetSoft on the topic of "Data Management Implementation." The speaker is Mark Flaherty, CMO at InetSoft.

Data Management Implementation Challenges

Cleaning up their data ahead of time will set the stage for more successful implementation.  Also with regard to data, they should probably assume the worst, and if they do start without an audit, which I don’t recommend, they should really build buffers in the project plan.  If you think that the cleaning up or conversion of the data will take three months, and you are wrong it only takes two then that’s great you can move stuff up. That’s probably better than thinking its going to take one month, and it takes three.

Moderator: Planning is key. When companies are preparing for implementation what do they need to do before they get started?  Besides assuming the worst when it comes to data clean up, what else should they do before they get started?

Flaherty: A pre-implementation audit is really essential. Identify any potential roadblocks. Do you have enough resources? Are there enough people on the team and do those people have enough skills? If you are running a weekly payroll, you are probably not going to get a lot of out of those people during the project because as soon as one payroll ends, they have a very short period of time before another one begins.

Also there are some people who are hell bent against a new data management system. You probably need to have a serious conversation with them before the project starts, and if they are not on board with it, then at the very least you don’t want them as part of the team, and you may not want them as part of the future organization. 

So there are a lot of things that organizations can do really before they get started. They can say, okay well it will take a year if we start now, and this is the goal I've set, then that’s etched in stone. It’s really just a blind guess about how long that will take, and they haven’t addressed some of the fundamental problems that will cause them to routinely miss their deadlines.

Moderator: When should a company consider hiring outside help to assist with the implementation process, and what are some signs that the expertise they have internally just might not be enough?

Flaherty: The answer to the first question about when they should hire an external firm is virtually always. And a client may have two or three people who might have worked with the information management system in some capacity, or they may even have recently hired a super-user. But it does benefit them to bring in somebody who is basically doing this for a living. 

Internal expertise may exist at some capacity, but in all likelihood, it is not the exclusive job of somebody to help with the implementation typically. End users have their day jobs, running payroll, processing vendor invoices, or doing technical work, but they are also assigned to the project.  If you don’t have people whose whole focus it is to assist with the project, things tend to slip often and considerably. When you are preparing for your future system, but a crisis comes up with your current systems, which one’s going to win?

So, it’s worthy investment to consider that outside help. In fact, in some cases vendors will not certify a client’s installation unless it is performed by that external firm.  So even if the client does have the internal expertise to do it, you certainly want to be above board with the vendor for the installation.

InetSoft’s StyleBI platform offers robust solutions for companies grappling with data management challenges, particularly those stemming from disparate data sources and complex integration needs. Many organizations, such as a manufacturing firm with data spread across ERP systems, IoT sensors, and legacy databases, struggle to consolidate and make sense of fragmented datasets. StyleBI’s Data Block technology addresses this by enabling seamless data mashups through a drag-and-drop interface, allowing IT teams to integrate structured and unstructured data from sources like SQL databases, CSV files, and real-time APIs without extensive coding. This capability reduces integration time significantly, enabling the firm to create unified dashboards that track production metrics, inventory levels, and equipment performance in real time, streamlining data management and improving operational visibility.

Data quality and governance are critical challenges, as inconsistent or incomplete data can lead to flawed insights and regulatory non-compliance. StyleBI tackles these issues with built-in data preparation tools that automate cleansing, normalization, and outlier detection, ensuring high-quality datasets for analysis. For instance, a healthcare provider dealing with patient records across multiple clinics can use StyleBI to standardize data formats and flag discrepancies, such as duplicate entries, before generating compliance reports. The platform’s role-based access controls and data lineage tracking further enhance governance, allowing administrators to restrict sensitive data access to authorized personnel and maintain auditable records for regulations like HIPAA, thereby reducing compliance risks and improving data reliability.

Scalability and accessibility also pose significant data management hurdles, especially for growing companies with fluctuating data volumes. StyleBI’s serverless microservice architecture, deployable on cloud platforms like AWS, dynamically scales to handle large datasets, such as real-time transaction logs for an e-commerce company, without requiring dedicated infrastructure. Its web-based, zero-client interface ensures that employees across departments can access dashboards on desktops or mobile devices, fostering self-service analytics that reduce IT bottlenecks. By empowering non-technical users to explore data and generate insights while maintaining robust security and performance, StyleBI transforms data management into a strategic asset, helping companies overcome complexity and drive informed decision-making.

Next: Data Management Implementation Strategies