Up Your Analytics Game with Data Transformation

This blog, co-written by Praveen Kumar (Data Architect, Apexon) and Chethan Laxman (SVP and Head of Client Engagement & Solutions Delivery, Gathi Analytics), draws on their joint experience in the data and analytics environments.

Remember Back to the Future?

Irrespective of the fact that this pop culture experience is approaching its 40^th birthday, it remains a great movie for many reasons, especially the way it explores how knowledge affects outcomes – past and future. Knowing what the future holds has always been a tantalizing prospect.

In business, the ability to predict the future accurately confers great competitive advantage – so it’s not surprising that enterprises are investing heavily in technologies that enable them to sneak a peek at what the future holds. Nowadays, thanks to the adoption of advanced AI-driven predictive analytics combined with real-time data processing in the cloud, depicting future trends is a realizable business goal.

Data scientists deploy many different modelling techniques in predictive analytics, including statistical models, decision trees, regression, deep learning, and increasingly neural networking to achieve insights about future behaviors. The basis for all predictive analytics modelling is good data. And the challenge facing enterprises is getting their hands on that good data.

Transforming Data into Future Insights

It is not that enterprises lack data. In fact, while today they have access to enormous amounts of data, their ability to put it to work for the benefit of the business is constrained by increasing complexity, limited skill sets and ill-equipped infrastructure.

A bit like time-travelling in the converted DeLorean, real future-gazing takes some preparation. Before embarking on a predictive analytics initiative, enterprises need to assess the state of their current data, and if necessary, transform it into a usable state.

Since much of the data that enterprises use is dispersed, harnessing it into an analytics-ready state is done using a process called ETL – extract, transform, load.

Data comes in all shapes and sizes, both structured and unstructured forms that include systems of engagement, systems of record and social media. So, the data transformation component is an essential part of the ETL process.

It’s when we convert the data from one format to another, typically from the format of a source system into the required format of a destination system. You will find that data transformation makes up a part of most data integration and data management tasks, such as data wrangling and data warehousing.

Increasingly ETL is taking place in the cloud. There are big advantages to this approach, including increased flexibility, agility, the ability to scale and faster decision-making.

However, for those used to a more traditional, manual approach to ETL, there are some considerations to bear in mind when using cloud-based data transformation tools for the first time.

• Enterprises typically need upfront provisioning for ETL and data analytics.
• Enterprises should keep in mind how much they’ll need to scale out to cater for the future demands of data.
• Ensuring high availability for analytics users helps timely decision-making.
• Operational overheads can be kept to a minimum by carefully managing the transition from development to production and ongoing maintenance.

As well as these cloud-specific considerations, businesses need to think carefully about the data formats they choose to leverage and how they expose the “truth” in the data in a meaningful way.

For a deeper dive into best practices on this topic, we recommend that you watch Apexon’s webcast Address Critical Shortcomings of ETL Tools for Data Analytics.

Deriving Value from Data – A Strategic Priority

However, data transformation for predictive modelling cannot solely be a techy initiative, run by the IT department. The process requires a strategic approach to data, not only because enterprises are surrounded by so much of it, but also that they need to think carefully about what to use.

Unless tightly managed with focused business imperatives in mind, data extraction and cleansing can end up being a slow, time-consuming process which has the potential to become a burden on the rest of the system, cause delays and potentially miss growth opportunities.

Cost is a key factor too: the size of an enterprise’s infrastructure will impact the data transformation requirements. For example, a bigger transformation project will likely require a team of data experts.

Despite these potential challenges, data transformation is not only a worthwhile project, it’s one of the most important drivers of business transformation that enterprises have at their disposal. Data transformation helps businesses increase agility, access insights faster, improve decision-making, speed up operations and guide innovation.

In addition, data quality is a key factor that drives the effectiveness of decisioning or value that the enterprise will be able to harness. As part of ETL, this makes it imperative that the quality of the data is accessed. Business rules must be put in place to maintain high data quality thresholds and associated data governance structure in place for continued effectiveness.

The question we need to consider is how can businesses up their analytics game with data transformation?

Let’s examine four approaches that are helping businesses take advantage of the powerful insights their data holds.

1. Is it Time to Create a Data Task Force?

Increasingly organizations are appointing Chief Data Officers to lead their data transformation initiatives.

Even if that’s not possible, a dedicated team focused on maximizing data value can ensure those goals are clearly defined from the get-go and the scope of work is manageable. Just as importantly, the team can focus on resourcing the initiative, planning future roll-outs and upskilling the workforce.

2. Start Small, Then Scale

For many organizations it makes sense to start with pilot projects where the ROI can be quickly realized. Whatever the size of the project, it’s important to have a clear understanding of the insights you want at the end and to stay focused on the goal.

In our experience, it can be all too easy to get distracted by the sheer scale of data in an organization and by “priorities” that are ancillary to the original project. And a quick word of warning: beware scope creep in analytics initiatives!

3. Finetuning Results

Models are not infallible – they’re made by humans. Once a model is up and running, it’s time to pay close attention to the results, looking out for any flaws in either the data inputs or the model design.

The best predictive analytics initiatives often need tweaking at this early part of the rollout process and if an initiative relies on live data, then assessing the model design will need to become an iterative process of continuous innovation.

4. Extracting Value with Data Governance

Well-governed data yields better insights and ROI.

It’s easy to think of data governance as a means of minimizing an enterprise’s exposure to risk, from a cyber breach, for example, or for reasons of regulatory compliance. But a data governance framework has the power to add value rather than just mitigate risk. For instance, the framework encompasses the entire lifecycle of data, including how the data is used and maintained.

Ultimately, really effective data governance results in better quality data that can be accessed more quickly and provides long-term ROI for the business.

Data: Use It or Lose It

Of course, you would expect a digital engineering services firm like Apexon to champion data transformation, but there is a caveat.

Data transformation is a critical stepping stone, taking masses or raw data and turning it into a usable form capable of delivering ongoing value. But what comes next is just as important as the ETL process. Turning data into actionable insights requires organizations to carefully – and creatively – calibrate how business owners access those insights.

As we noted above, the quality of the data itself has to be a prime focus, with processes, techniques and tools in place to identify quality issues and fix them in a timely manner. This also requires a well-established Data Governance framework to be available to stakeholders.

Data visualization is a key component in the success of any analytics initiative. For this reason, at Apexon, data transformation goes hand in hand with data visualization techniques such as intelligent, interactive dashboards displaying key performance indicators that empower employees to take data-driven decisions.

After all, what’s the point of generating insights that do not get used? They may as well be gathering digital dust. And while data transformation and quality assessments are critical, to ensure data is transformed into actionable intelligence, it needs to go beyond being decipherable by the precious few, to being easily accessed and intuitively understood by all those who touch it.

Data can only be truly transformational when it enables decision-making. Marty McFly may have made some questionable decisions when he was stranded in the 1950s, but he didn’t have the data that he needed to make the right choices.

In the not-too-distant future, the companies that leverage the data they have to generate actionable insights are the ones that will reach the required level of digital maturity. Data is power, but you must take advantage of the actionable insights, otherwise your digital journey is going to take more than a rebooted flux capacitor to be a success.

 Data transformation is an essential step in the journey to digital maturity. By creating a robust foundation for data analytics, Apexon puts organizations in the driving seat of their digital journey.

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