3 Common Practices Inhibiting the Potential of Your Data Analysis Tools
The magnitude and complexity of modern data analytics have the potential to elevate enterprise forecasting and decision-making processes to a whole different level. Yet not every organization is immediately prepared to maximize their data’s full value once they have finished building a big data platform.
Prompt and actionable predictions require the right conditions to foster reliable insight from the start. Unless organizations pivot away from these three bad data practices, any data analysis tool they implement will face longer latency before reaching its full ROI.
Bad Habit #1: Having Multiple Definitions for the “Same” Data Element
Raw data is messy in its natural state and relies on how meticulous users are while gathering and uploading it. The risk for that data to be wrong, incomplete, “redefined” or of inconsistent quality has inhibited the extent to which some departments explore or fundamentally use petabytes worth of data. Unable to transform data sets into data assets, they remain stagnant and miss opportunities to leverage valuable predictive insight.
Though data management becomes more complicated with different data of varying quality lumped together in the same place, strong data governance can help catalog the different datasets and minimize the risks by quarantining bad data early. Organizations, not just individual departments, need to instill governance practices around the entirety of their comprehensive data. That includes but is not limited to the following practices:
- Developing a Governance Framework – A thorough framework sets manageable parameters up front, creates a process for resolving issues with data quality, and enables users to make decisions that leverage the assets themselves.
- Launching a Data Quality Management Program – The first step is for organizations to determine their own measurements and definitions of data quality. Then, there needs to be a regular process of exploration, analysis, guideline creation, monitoring, reporting, and issue resolution.
- Mastering the Data – Master data management helps organizations define and maintain accuracy and completeness of their data in a way that synchronizes across departments. When done right, it creates a shared reference point that mitigates the risk of data quality being compromised.
Bad Habit #2: Keeping Data in Silos
While departmental thinking offers priceless specific subject matter expertise, siloed thinking can narrow the scope of data analysis. In the past, the challenge of interconnectivity of data between different departments limited the full potential of predictive models. Healthcare organizations need the ability to compare and analyze data ranging from patient records and CDC reports to workforce trends and occupancy rates. Financial service companies need the ability to compare and analyze data ranging from customer demographics and customer service indexes to account openings and debt to asset ratios. The central consideration is that each industry has its own range of intersecting data that might be missed if that data is kept apart. That is why breaking data silos is so important.
The growing alternative to fragmented data warehouses is using a data lake as a single point of storage. Both structured and unstructured data can be thrown into one undiscriminating repository.
Bad Habit #3: Thinking Short Term with Your Data Analysis Tool
Some businesses react to immediate analytical needs by taking discrete action for a specific department. The problem with that is they provide a stop gap data analysis tool rather than seeking out a long-term, unified, and cost effective solution. Building individual datamarts for each department rather than a comprehensive system makes redundant work for developers or data scientists. Exhausting all of your effort preparing data before putting it into a data lake takes vital time away from predictive modeling. There are plenty of examples of this reactionary approach to data analysis, and all of them miss the big picture.
A central big data platform built with an understanding of your current capabilities and your long-term business challenges will have the greatest ROI. Yet the process is not without its obstacles. Any organization looking to find the right big data platform will encounter obstacles that threaten to cost millions while preventing them from achieving a finished product. Worst of all, most of these are completely avoidable.
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