Posted on April 20, 2020 by staff

Harnessing transformative power of data science


From healthcare to manufacturing to retail, data science is playing a crucial role in helping teams to understand how to effectively use their databases, usually used to store a collection of facts about customers, sales transactions or web interactions.

It finds patterns and trends hidden within it, delving into the why and how a specific action was taken (i.e. a purchase decision) and it tries to understand the context.

Big Data seems to be an easy solution, however, too much data is overwhelming and acts as a hindrance to momentum. The right big data tools not only allow you to see the value of precious data at your fingertips, but also give you valuable insight and predictive powers not possible in their absence.

Data collection done well will be a key foundation of your digital landscape and an important catalyst for future digital transformation. Storing such treasure trove of data will have cost implications but consider it a savings account for the future.

When the time comes for intelligent technology such as algorithms to be applied, those algorithms will be more accurate the more data they have access to.

Big Data needs the right data skills

Aggregating different forms of data can be messy, and can result in unmanageable volumes, also known as: Big Data.

The life cycle of usable data usually involves collection, pre-processing, storage, retrieval, post-processing, analysis, visualisation and so on. Traditional applications cannot effectively process these high volumes of data.

So how do you find the right models and data processing techniques and know how to apply them correctly? Building an internal Data Science and engineering team is the obvious answer, incorporating two core skillsets that are likely not currently part of your existing software engineering teams.

Data scientists bring the strong mathematical understanding to help ideate, model and build complex algorithms. Data Engineers compliment this by bringing the skills and experience of using the platform tools which enable algorithms to be appropriately developed and unleashed on large amounts of data.

However, the recruitment process can be extremely time consuming due to the growing gap between supply and demand for Data Science skills, meaning it can get very competitive to find and hire the right resource.

Outsourcing data science solutions offer benefits including access to global talent pools, essential generalist skills and expertise that can solve real and specific business needs. In addition, businesses can leverage third parties’ larger volumes of data (i.e. unstructured data) and combine skills and cutting-edge technologies not yet available to them.

Investing in Machine Learning

Once you have the essential foundations of data and the right talent to manage it, it may then be the right time to invest in a machine learning solution, a clever tool that processes the ‘automated & actionable insights’ that are often invisible to the human eye. However, having a solid strategy for the entire project is essential.

AI and machine learning can be complex, so ring fencing those elements makes sense to contain that complexity and hone simple standard engineering mechanisms. Understanding the power of algorithms can also be abstract, each with their own strengths and weaknesses around a variety of given problem types. It is therefore important to have the insight to know when and where AI or Machine Learning should be used as some issues are more efficient and accurate without AI.

From the project management perspective, there are Data Science methodologies built to provide a lifecycle to Data Science projects.

The ‘Team Data Science Process’ method, also known as TDSP, outlines five steps that are usually taken when executing a project of this scale. These include:

  1. Clear understanding of business and requirements – The business need is identified and business goals are defined. At this stage, teams will have to determine whether they need additional data from different data sources. It’s essential to make sure that all project parties clearly understand what they are trying to achieve/solve for the implementation of the technology.
  2. Data acquisition and understanding – The current state of data is assessed, explored, pre-processed and cleaned. At this stage, Data Scientists usually have a better idea as to whether existing data is sufficient or not.
  3. Data Modelling – Feature engineering is performed on the cleaned dataset to generate a new, improved, data set that facilitates model training. At this stage, the difference between generating the input data needed by a Machine Learning model for a Proof-Of-Value and doing it continuously and at scale is important.
  4. Deployment of the data pipeline and the winner model to a production or production-like environment. Model predictions can be either in real-time or on a batch basis. The latter should be decided at this stage. After ensuring the dataset is comprised of (mostly) informative features, several models are trained and evaluated, and the best one is selected to be deployed.
  5. The last stage is Customer Acceptance – Two important tasks are performed, namely system validation and project hand-off. The aim for this stage is to confirm that the model deployed meet the client’s needs and expectations. The project hand-off includes deliver the project to the person responsible for running system in production, delivering project reports and documentation. At this final stage it is important that the third parties used, provide the right training to all the team members accordingly.

Ultimately, data is the key to operational and digital transformation and harnessing the transformative power of Data Science requires the careful consideration and deployment of a water-tight strategy.

There needs to be a disciplined approach to the capture, management and processing of all types of data to avoid being overwhelmed by its mass, and more galvanised by its potential.