Applying a data-driven approach to boost organizational efficiency

As businesses mature and their operating landscape becomes increasingly competitive, what previously might have been a “gut” decision, is no longer applicable. Data and empirical evidence begin to take precedence over intuition.

It is more important than ever to start thinking about creating a data culture where experimentation is prioritized with privacy and security in mind.

Importance of creating a data culture

Companies can start to heavily benefit from a strong data culture, leveraging and utilizing it as the foundation to drive the decision-making processes. Having good data hygiene pays dividends later on, as businesses can leverage the abundance of data previously gathered to create new products, optimize their business, compete in new markets, and improve existing experiences.

An example of this can be seen with Uber. Strong data culture in the company helped them leverage storing and utilizing historical data in order to improve the Uber Rider app.

Netflix is another example of a data-driven company from its inception. Their blog is filled with examples of how analytics can help drive business decisions.

How can a company start thinking about creating a flourishing data culture?

Creating a data culture shouldn’t be driven by any single department, as this creates data silos, but rather it should be part of the DNA of the company, and if needed steered by engineering.

• Investing in the right tools and infrastructure

Infrastructure should be an enabler rather than a show-stopper, it’s important to understand what is required from the start as pivoting the entire infrastructure should be avoided when possible. Modern data stacks can dramatically streamline the process of managing and interpreting data. Tools such as Apache Flink - can enable real-time data processing. Modern storage solutions such as Databricks, Delta Lake and Snowflake make storing data much more effective than before.  

• Democratizing data

A common pitfall data organizations fall into is creating artificial data silos, where each department owns their data and sharing starts becoming cumbersome. Copying data from one database to another to analyze and create insights can quickly become a heavy overhead. Investing in the right tools ensures that data is shared across organizations. In doing so, you empower the broader organization to be able to analyze the data, make inferences, and implement beneficial change.

Oftentimes, the most impactful insights come from unexpected places. You may be surprised when some of the most useful data and metrics are gathered by very distinct departments - marketing, customer engagement, and operations.

Experimentation

Once a strong data culture has been established, the next step is to act upon the data gathered. This process can be further subdivided.

Data understanding

Referred to as EDA, exploratory data analysis is often the entry point to data in any system. This process enables you to become familiar with the data, as well as discover data shapes, spot anomalies, understand data types, and much more. To get inspiration about EDA, Kaggle is a good resource. There are also a few open-source projects which help with the data analysis.

Data quality

One of the hurdles in embracing a data culture is that not all data is created equal, that’s why understanding data plays such an important role. In order for data to be considered an asset, data must be valid and reliable. Each organization is different and must implement strategies they resonate with. However, a good rule of thumb is to think about the following:

• Data quality standards - defining what constitutes “good, actionable data”, this may include things such as consistency, completeness, relevance, and so on.
• Data governance - establish a program to oversee data management, enforcing policies and quality standards.
• Define metrics - develop a suite of metrics to measure data quality over time, ensuring these metrics are not skewed as the system evolves over time.
• Data tools - invest in quality data tools that ensure monitoring, enrichment, and data cleansing.

Standards defined in this process can later be leveraged in more complex data pipelines, making it the foundation of a company's data strategy.

Embrace the empirical method

With a good understanding of data and a solid data quality plan, organizations can start thinking about leveraging their data. A good approach is to define reproducible processes and verify results. Organizations can think about the end result as a KPI - the end goal being to optimize for the indicator or metric.

This procedure usually consists of 5 stages:

• Discover data sets: Utilize previous data points to identify and collect relevant data sets - both internal data (sales data, customer and production metrics), as well as external data (market trends, industry reports). Choose data that compose the metrics that are being tested.
• Create a hypothesis: Hypotheses should be designed to address specific business questions and test possible scenarios around the metrics which have been previously selected. 
• Test: The next step is testing the hypothesis. Depending on the metrics selected, this can be done in a controlled environment. One example of a controlled environment would be only launching the experiment in a specific region, or for a narrowly defined subset of users. A/B testing can be used to limit the users exposed to the study. 
• Monitor: Monitoring the impact of the tests will yield more data to further back the study.
• Iterate: Based on the results of the tests, strategies can be refined or completely discarded. 

In the end, the framework leaves the opportunity to continuously learn from your data and evolve your strategies.

Privacy and security

It’s important to treat data in accordance with your domain. In any organization, substantial consideration should be given to data confidentiality and safety, as there are multiple regulators which must be adhered to.

In Europe, there is GDPR - and it gives control to individuals over their personal data. When designing data systems, designing with GDPR in mind can help save countless hours of rework later on.

There are also domain-specific data regulatory standards such as HIPAA in the United States, which outlines data privacy and security provisions for safeguarding medical information, such as: establishing standards that protect individuals’ medical records, enforcing standards of protecting said data and ensuring confidentiality as well as require standardized electronic formats.

Keeping privacy and security in mind when leveraging data in any organization is critical as it will not only protect the organization from possible liabilities later on but also avoid re-work if the organization wants to enter new markets, where privacy and security are regulated by law.

How engagement with Transcenda can help

In today's fast-paced and data-driven world, embracing a data culture is one way for organizations to stay competitive. Above, we’ve outlined key components to keep in mind, when embarking on the journey to understand customer preferences, optimize operations, and gain a competitive edge in your respective industry.

Transcenda helps organizations leverage data to drive organizational efficiency. We promote a hands-on approach where data engineers, data scientists, product, and other stakeholders collaborate, from discovery all the way to implementation and monitoring. We strive to gain actionable insights from data and unleash its full potential. Contact us to find out how we can put our expertise to work for your organization.