Features
Feature
Get in the Mix
What mid-market companies should know about data management.
by Lyndsay Wise
Despite the buzz surrounding the importance of managing data and the use of data warehouses, mid-market companies have historically been left out of the conversation. But no organization, regardless of size, can afford to ignore this reality of the business world.
Companies that lag behind frequently suffer from a lack of visibility into their customers, supply chain, products and services. They can’t clearly see how well they perform as an organization and against their competitors. Without proper structures in place, accessing the information required to understand these challenges proves difficult at best. Addressing business pain points requires a data management infrastructure.
At a fundamental level, access to timely and valid information translates into the ability to make better decisions and manage ongoing processes and performance. Consequently, organizations that value and leverage the data collected and stored within their companies are better poised to drive their business to the next level. As part of an overarching data management infrastructure, they most often implement a data warehouse to provide a central access point for analysis and better decision making.
What this entails will differ depending on the type of business, how it is managed, and how information is structured and accessed. But in many cases, implementing a data warehouse helps create a consolidated view of what is happening within an organization. Supporting tools then enable people to access a broad set of information more easily and automate the analysis process to improve monitoring and metrics management.
While different-sized businesses might address issues differently, they all require the same tools and solutions.
Reality Check
So what inhibits smaller organizations from adopting a comprehensive data infrastructure? Too often, misperceptions become reality. Until recently, many simply considered a data warehouse outside the realm of possibility because of perceived barriers such as time of implementation, overall cost and maintenance requirements.
However, the realization has come that while different-sized businesses might address issues differently, they all require the same tools and solutions. As a result, small and mid-sized organizations are increasingly embracing BI, empowering these enterprises to shift their focus from reactive management to proactive action.
Now, in addition to dashboards and analytics providing access to better insights, data warehouse solutions have become more accessible because of lower costs and maintenance fees. This, in turn, enables these organizations access to the same types of information to address issues in much the same way larger companies do.
Question and Answer
Now that organizations can more easily enter the data warehousing game, the question becomes, “Why should we?” The value of an implementation may not be obvious to those that already produce daily reports or can conduct analyses by compiling data through spreadsheets. And for private companies, data consistency and the ability to audit or analyze usage patterns might not be considered mission-critical.
The answer to this question is simple. To truly be effective operationally requires more than a daily snapshot of what is happening related to sales and logistics. Additionally, having analysts react to issues as they arise is not an efficient use of their time. It’s more effective to employ resources that can predict scenarios and look at what might occur to anticipate issues before they become problems. Finally, consistent data—stored and man-aged within a single platform—delivers a broader view of the business, enabling companies to develop and maintain insights they couldn’t otherwise.
Identify the Right Solution
Once organizations commit to the concept of data warehousing and developing a data management infrastructure, they must explore many considerations before choosing a platform:
- To appliance or not to appliance. In many cases, the ability to plug and play is beneficial for companies. Appliances can provide this ease for companies, whereas in-house data warehouses might be more flexible to specific or unusual business requirements.
- Data volumes. Identifying the amount of data that needs to be collected over time affects the type of data warehouse required. Some solution providers are known for the volumes they can handle, while others are focused on providing quick query speeds. Only a select few deliver both.
- Speed and analysis type. Looking at the type of analysis required, the volume of daily analysis and potential service level agreements (SLAs) to meet will affect an organization’s overall software choice. Certain data warehouses are optimized for high query performance, whereas others are optimized for online analytical processing (OLAP) and other forms of analytics.
- Internal infrastructure. Consider what already exists in-house. Depending on the systems already in place, their structure and the databases they originate from will affect integration, implementation and, potentially, maintenance. Although there are commonly supported databases, companies with legacy systems—built on different platforms or that have specific requirements—should make sure the data warehouse they select can support the current infrastructure without workarounds or implementation roadblocks. Otherwise, implementation times and maintenance efforts may be too great to justify the solution choice.
- Future growth. It is important to look beyond initial implementations, toward foreseeable business goals or desired strategic goals to anticipate future requirements. This means taking into account upcoming expansion through organic growth or mergers, additional analyses, increased data volumes, etc.
First Steps
In highly competitive markets, mid-sized organizations cannot overlook the importance of building and maintaining a proper data management infrastructure. Doing so will drive better performance management and strategic decision making. By adopting a data warehouse, organizations can begin to put the proper infrastructure in place to achieve these goals.
Identifying the type of solution, the type of data sources, overall volumes and frequency, types of analytics and metrics required, and anticipating future needs are the first steps. They will lead to the deployment and maintenance of a successful data management infrastructure that will result in better visibility and performance management.
Lyndsay Wise is the president and founder of WiseAnalytics. She has 10 years of IT experience in business systems analysis, software selection, and implementation of enterprise applications.