Data Is Not Just Our Sweet Spot – It’s Yours
Data, data, data. It always comes back to the data. Everyone’s got it, everyone needs to protect it, and everyone wants to make the most of it.
Every IT initiative, whether it’s a routine hardware refresh, a transition to the cloud, a legacy system upgrade, or a new system rollout, involves data management, and our team of data strategists and programmers has done some extraordinary work in the data management space.
At the center of leveraging the current and potential value in your data is Business Intelligence (BI). Often referred to as query-able access to business data by business users, BI applications such as cubes or data mining tools provide powerful reporting and analysis capabilities that help businesses harness the vastness of their data and use it strategically to inform decision-making.
Master Data Management
We hear all too often about the frustrations of disparate systems: data inconsistencies, data redundancies, lack of interoperability, wasted employee productivity, misspent IT resources, and most of all, the opportunity costs that mount up with continued reliance on these siloed systems.
The Mind Over Machines approach to Master Data Management (MDM) addresses these very familiar IT frustrations by:
- Defining enterprise data
- Finding the data sources
- Mapping the data to the universal meaning
By creating a common language that is shared across many slices of the enterprise, you can address data compatibility and redundancy issues while improving productivity, thus freeing up IT resources to focus on strategic, value-add solutions.
Read more about our integrated approach to KPI and MDM in the healthcare industry in our blog post, Let Them Eat KPI. The results? Dramatic improvements in efficiency; long-term viability and integrity of system output; and extensive new capabilities for exponential growth in data tracking.
Data Security Management
Protecting your data and ensuring compliance requires a centrally managed data security framework. Mind Over Machines will implement this framework, tailoring it to meet your industry-specific compliance needs (eg., Sarbanes–Oxley, FINRA, HIPPA) and ensuring that it will:
- Acknowledge the value of your data
- Maintain the integrity of your data
- Mitigate the risks associated with your data
- Protect your sensitive, mission-critical data
- Ensure that only authorized parties can access it
- Reduce your susceptibility to data corruption
Your enterprise data security plan can help you better identify which types of data are critical to your business, including those that will drive future revenues, as well as help you mitigate the risks associated with the loss or corruption of that data. You’ll also ensure adherence to the policies and procedures required to stay in business – and out of trouble with regulators.
Data Quality Management
No matter the industry or the type of enterprise system, if humans have gotten involved with your enterprise data – directly or indirectly – the quality of data will be suspect. For instance, if pull-down menus are no longer sufficient to capture critical customer feedback, diligent customer service representatives (CSR) might work around the system and put additional information into comment fields. However, getting information out of those comment fields is difficult because the language isn’t standardized. Despite well-intentioned CSR efforts, the quality – and usefulness – of the data is negligible.
We recommend that you either fire all the humans, or perhaps, in less drastic and more strategic fashion, let us help you take the following steps to leverage your most valuable assets:
- Conduct an analysis of the processes that create the data
- Calculate the cost of the “unclean” data
- Determine the costs of fixing the data
- Change the process and/or implement data quality tools
Data Architecture, Design and Analysis
Getting out-of-control data under control starts with a process, just like any software engineering process:
- Define the problem: My sales data is a mess and I need help to make it bigger or better or faster.
- Define the scope of the problem: There is likely more than one problem with messy sales data.
- Identify the audience: What roles will this information influence?
- Define a goal: What do you want from your solution?
- Envision what “solved” looks like in terms of sales, operational efficiencies, etc.
In the big reveal, you now get value from the data locked up in your enterprise, and you can make it manageable, scalable, affordable, and usable.
A big data approach starts with the basic notion of storing everything for later analysis — because once that data is gone, it’s gone. This represents a paradigm shift from a traditional data approach, whereby you first figure out what you need and store only that.
Big data is but one of many exciting approaches to data management (and not always the right one) that Mind Over Machines will consider when determining which type of data solution will reap the most rewards for your organization.
Here’s a sampling of some of our favorite – and most powerful – data projects:
We envisioned and developed a datamart that enabled real-time analysis of hundreds of weather data sources, creating new capabilities for the U.S. Army to deliver accurate and up-to-the minute reports on weather conditions affecting military performance — often a life and death concern in combat zones. Learn More.
We built and integrated a visual mapping interface into a data analysis engine – and drove revenue growth for our client from $200 million to $3 billion. Learn More.
We mashed multiple public and private databases and created an innovative prospecting system – and our client realized more than 5000% return on investment (ROI) before we stopped measuring. Learn More.