Eliminating Major Barriers for Data Insights
The lifecycle of Data, Data Analytics, and Data Science starts with collecting data from relevant data sources, performing ETL (Extraction, Loading, and Transformation) functions, cleaning, and enabling data in a machine-readable format. Once the data is ready, statistical analysis or machine learning algorithms can identify patterns, predict outcomes, or even perform functions using Natural Language Processing (NLP). Since data is at the core of data analytics, it is imperative to understand the challenges we possibly might face during its successful implementation. Here we present the top four data challenges :
Complexity: Data spread across various sources
Merging data from multiple sources is a major challenge for most enterprise organizations. According to McAfee, an enterprise with an average of 500 employees can deploy more than 20 applications. Larger enterprises with more than 50,000 employees run more than 700 applications. Unifying the data from these applications is a complicated task that can lead to duplication, inconsistency, discrepancies, and errors. With the help of data integration and profiling, the accuracy, completeness, and validity of the data can be determined.
Quality: Quality of incoming Data
One of the common data quality issues in the merging process is duplicate records. Multiple copies of the same record can lead to inaccurate insights as well as computation and storage overuse.
What if the collected data is missing, inconsistent, and not updated? Data verification and matching methods need to be implemented at each collection point to prevent flawed insights and biased results.
Volume: Volume of data available
To find relationships and correlations, a successful machine learning algorithm depends on large volumes of data. Data collected from multiple sources and multiple time frames is essential in creating machine learning models during training, validation, and deployment phases. More data does not necessarily mean gathering more records but can mean adding more features to the existing data from different sources that can improve the algorithm.
Algorithm: Conscious effort to remove confirmation bias from the approach
The major advantage of AI over humans is garnering insights into an algorithm’s decision-making process (using explainable AI). Furthermore, algorithms can be analyzed for biases, and their outcomes verified for unfair advantages to protected classes. Although AI, on the onset, can be viewed as perpetuating human biases, it offers better insights into the data and decision-making process.
Over the last decade, Allwyn has surpassed these common Data challenges with the proven experience of its seasoned Data professionals. We will share our own Data Management Strategy in next week’s post. Watch this space or follow us on LinkedIn to stay tuned.