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Is Your Organization Data-Distressed?

Does this sound familiar?  

Your organization is ready to develop more increasingly sophisticated analytics, but finds it difficult to get its data all in one place in a form that is usable. It is probably not a stretch to imagine this is poor quality data, filled with errors or incomplete. Also, there may be a reluctance to share data across the organization. The data may not be collected in a consistent way, or it might be locked in rigid data silos that are difficult to connect. 

The truth is this scenario is more common than you might imagine, from large corporations to small businesses. The reality is it is not easy to pull together the right data, the right way, across the entire organization.  

Recognize that no organization instantly becomes adept at the next generation of analytics. It is a journey, and like any journey sometimes the path is not obvious. It can be difficult to navigate. 

Where are you?

It is essential to understand where one is in the analytic journey. Most of the time in a data-distressed organization you are in the beginning stages of the journey. Even organizations that have experienced success in pilot programs or proof-of-concepts may start to notice a slowdown in analytic velocity. This may be the case because they may not understand how to translate all the elements in place: people, process, technology. 

Establishing a baseline is critically important. It helps you know the type of data, the quality of the data, and the speed of that data. If you do not have a baseline, you simply will not know if you are going backwards, forwards, or sideways in your analytic maturity.  

Getting your house in order

If knowing where you are is the first step, the second is most certainly getting your house in order. Your organization may want to develop increasingly sophisticated analytics, but is stymied by an inability to get the underlying data in order. Many organizations leave the IT department alone to deal with developing what is needed for analytics. They may trust that the IT department is the absolute expert when it comes to all things technology. In many cases, innovation can slow down – if not altogether stop – in the context of a misaligned IT department. Organizations should help IT departments understand that they are a support function to the broader organization. Businesses of all sizes have to keep IT departments accountable to the broader vision of bringing together a variety of data sources to create a more comprehensive view of data. 

William Drury's law of zoology states that all animals are presumed to be smart until proved not to be; some corporations have IT departments that seem to operate in the opposite of that belief when dealing with outside departments. If the IT department is stating it may be too difficult to bring data together to support your analytic vision, or they remain closed-minded about progressive vendor solutions claiming them to be “trendy,” it may be time to investigate alternative talent to bring about the organization maturity you desire.   

What to do 

Bringing the stakeholders together is almost always a good thing, especially in creating never-done-before capabilities in your organization. Your aim should be to produce collaboration and build relationships. This is important for every department, but especially between IT and your analytics department. Fertile ground must be cultivated for massive innovation to occur between departments that can actually use the data. If it does not seem like you can spark collaboration out of the gate, know that this is normal. In many cases, this is because departments have not been challenged to think outside of itself and it is a new practice. Each group are experts, but unless they can make compromises, they will harm the organization and remain an island of expertise marginalizing their impacts on the organization. Treat collaboration between groups as a seedling that, for a while, you have to provide intense care for, knowing one day it will be able to care for itself.  

In the Real-World 

Applying these methods in real-world organizations can certainly be difficult, but it is achievable. Once the organization has an awareness that what is being recommended is a key competitive advantage rather than a luxury, things have a way of gaining significant velocity. Of course, the more you have the right team members in the right seats on the proverbial bus, the universe has a way of coming alongside you to help your company get traction.  

Simply put: get the data in order by making sure that the analytics department is included from the beginning in designing data management frameworks. No company wants to spend huge sums of money getting the data together in an unusable format simply because the end-user was not involved throughout the process. While I understand there are several long-held ideas that linger in an organization's nooks and crannies, the notion that the data solely belongs to the IT department should not be one,  especially since the analysts are the users. Time and time again I see problems arise when the point of view of the data scientist, analytics department, or business analyst was not taken fully into account. This is a mistake – and one that is costly.  

Take the time to encourage collaboration and relationship building. Remind all employees that each department should be sharing a common goal: to fulfill the business strategy. Move your organization out of a data-distressed mode and into one of innovation and a brighter future.  

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Comment by Richard Ordowich on September 14, 2015 at 7:10am

Unfortunately most organizations build data processing systems rather than knowledge creation solutions. As a result the data that is created is unsuitable for use by humans to obtain knowledge.

A great deal of time is spent "pushing data" from one environment to another. From a transaction system to a data warehouse. From a data warehouse to spreadsheets and business "intelligence" applications. We then attempt to integrate data from data silos and standardize data using master data management.

In manufacturing this is referred to as rework. We keep reworking the data product until it is fit for consumption. We don't design quality data or quality data processing systems. Until we change how we design data and focus on knowledge creation rather than data processing we will remain Data Illiterate.

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