Enterprises large and small need to use all means at their disposal to gain a strategic advantage for their business. To remain competitive, these organizations must make use of contemporary machine learning tools to unlock value and meaning from the wealth of data they have about their customers, products, employees, work processes, and even competitors. Whether their customer service department wants to look at how their customers feel about their products, or their sales and product design teams need to analyze what products would sell when, to whom, with what features, and at what price point, advances in machine learning can enhance existing data models and take this decision making to the next level.
So, what do businesses need to unlock the value in their data? After all, most machine learning tools are open source and there are plenty of commercial machine learning platforms that promise to run a number of models on your data. Then why is it so hard to adopt machine learning?
It’s a numbers game
As is so often the case, the answer lies in numbers. Machine learning algorithms crunch numbers, so the transactional and analytical data that enterprises have in their databases and data warehouses needs to be selected and prepared to be fed into these algorithms. There are several data preprocessing steps, from data imputation that addresses data sparseness to techniques for normalizing and standardizing data, allowing the appropriate machine learning model to be applied.
Data Imputation and Normalization
Common techniques for data imputation provide default values, such as 0 where none exist, or correct for erroneous and outlier data, thereby reducing “data noise”. The process of standardization and normalization utilizes a number of techniques for data preparation based on the type of machine learning model that needs to be utilized for a given business problem or question, such as label encoding and one-hot encoding to transform textual data values into an appropriate set of numeric values that do not introduce artificial correlation within the data.
Other data normalization techniques involve reducing the amount of data that needs to be presented to the machine learning model so that the models can efficiently process the data and, more importantly, only consider the data that falls within the business domain of the question or problem being solved. Common data reduction techniques involve reducing dimensionality by creating composite dimensions and by feature engineering, which looks at statistical characteristics of the data like standard deviation, entropy, and correlation to identify the set of data fields that are important for the machine learning model to consider.
The value of skilled data scientists
Popular data scientist surveys indicate that all of this preparation constitutes about 70 to 80 percent of the work that goes into developing and running machine learning models. Once the preparation is done, machine learning models can discover myriads of data permutations and combinations that contribute to the answer the business is looking for.
All of this work requires a trained data scientist with a keen understanding of statistical and mathematical data manipulation techniques, technical skills in data processing tools like Python, R, sklearn, and pandas libraries, and in case of big data tools like Apache Spark, the ability to quickly prepare the data set and select relevant machine learning models for enterprise data. Visionet’s advanced analytics team is well-versed in these data processing and analysis tools, offers strategic advice to its customers on setting up their machine learning-based analytics programs, and provides a deep bench of onshore and offshore data scientists to do all the preparatory work in an efficient and cost-effective manner.