Data-driven decision making (DDDM) provides facts and data that support companies in making strategic decisions that yield the most benefits. Leading global enterprises such as Google, Gogo, and Netflix fully embrace data-driven decision making, proving it to be an essential part of any successful business.
Are you still on the fence about the benefits of data-driven decision making? Unsure about the steps or hidden pitfalls of implementing it within your company? Let’s explore how to successfully establish a culture of data-driven decisions and see what we can learn from a real-life success story.
Data-driven decision making at work: a success story of Gogo
While we could create a long list of the benefits of data-driven decision making, let’s instead take a look at a real-life example that demonstrates the value of DDDM. The example in question is N-iX’s partnership with Gogo - one of the world’s largest providers of in-flight connectivity and entertainment services.
Under the microscope: Gogo’s goals and challenges
Gogo aimed to expand their customer reach by improving the quality of provided services. However, the company was facing a serious bottleneck caused by performance issues of their equipment.
Gogo’s satellite antennas, which are installed on over 6,600 business aircraft, often malfunctioned and had to be removed for maintenance and repair. What’s more, once Gogo’s technicians removed and checked the equipment, very often they were not able to identify any malfunctions or anomalies (this is also known as no-fault-found or NFF). This incurred significant penalties that the company had to pay to their clients for service interruptions.
Naturally, the company wanted to identify and remove the cause of these NFF maintenance requests. Since the reasons behind malfunctions were only hypothesized, Gogo required proof before making any decisions on approaching the issue.
Using data to improve performance and service quality
Gogo partnered with N-iX to create a single unified platform to collect data from their equipment. N-iX experts developed an end-to-end data delivery pipeline, which orchestrated data collection, analysis, processing, and storage in the data lake.
Next, our data science engineers applied the necessary models (such as Regression Analysis) and Machine Learning algorithms to analyze data from antennas, monitor their health, and predict malfunctions. As a result, we identified the cause of NFF failures (performance was affected by the antifreeze liquid that was getting inside the equipment) and provided factual evidence that helped Gogo take the necessary steps to fix the issue.
Further improvements with data-driven decisions
Our team also uses the collected data to provide comprehensive reports on financial (purchases, data consumption, etc.) and operational (WAP, modem performance, etc.) aspects of Gogo’s equipment. These reports allow the company’s C-level management to make data-driven decisions on how to address the main pain points of their users. This results in an increased quality of the provided services, which, in turn, helps Gogo expand their customer reach.
The main takeaway of Gogo’s success story is that, while the process of establishing the culture of data-driven decisions is not an easy one, the end results are well worth it.
How to implement an effective data-driven decision making process
Just as Rome wasn’t built in a day, establishing an effective culture of data decision making takes time, effort, and precision. To achieve it, the following steps must be taken:
1. Set specific goals you aim to achieve
Before you begin implementing the data-driven decision making process within your organization, it is important to determine the end goals that you want to achieve with it. It will bring the focus to your project and provide you with something concrete to measure your results against.
A key point of this step is making each goal as specific as possible. If this proves to be a challenge, there is always an option to engage an external team of experts. This team can help you validate your idea, set realistic KPIs, and prepare a detailed transformation strategy.
2. Establish an end-to-end data delivery pipeline
This step involves establishing an effective delivery pipeline that will provide you with relevant data and enable data-driven decision making.
Start by analyzing the existing systems, as well as identifying the sources of your data and the type of data you want to collect. Use the findings to transform your infrastructure into an efficient end-to-end pipeline that will orchestrate the data flow within your organization and store it in a dedicated data lake.
It is highly advisable to tackle such projects by partnering with a reliable tech service provider who has relevant experience in Big Data, Data Science, Business Intelligence, and DataOps. Such partners can audit your systems, create a detailed transformation plan, and give suggestions on how to make it as efficient as possible. Furthermore, they can provide you with a team of experienced data engineers that will facilitate your DDDM process.
N-iX’s partnership with a Fortune 500 industrial supply company (under NDA) serves as a perfect example of how an efficient data pipeline can benefit a business. Our engineers developed a single unified data platform that improves the efficiency of data management, enables predictive analysis of inventory-related expenses, and optimizes costs. You can read more about our partnership here.
3. Perform data analysis
A data delivery pipeline ends with a thorough analysis phase. This is where the data that was collected and processed is turned into actionable insights that can be used as a basis for decision-making.
The consequences of choosing the right partner are the most prominent during this step. The results depend heavily on the choice of the right tools, technologies, and best practices to be applied to your project.
4. Turn insights into action
In this final step, you apply the insights obtained from data analysis in strategic planning sessions. Similar to Gogo, the gained benefits of data-driven decision making (DDDM) can be numerous, from reducing expenses and raising profits to better-quality service and increased market reach.
Bonus step. Review your progress and make improvements
Data-driven decision making is a continuous process that requires regular review. After a certain previously agreed-upon period of time, it is crucial to review your progress and see the tangible impact of DDDM. If the results do not match your expectations, go through the previous steps again and explore what possible improvements can be made.
Top challenges of data decision making and how to avoid them
Successful adoption of data-driven decision making comes along with some challenges. However, with the right approach, all of them can be solved or avoided altogether.
Challenge 1. Using irrelevant data.
One of the most common challenges involves using the wrong or irrelevant data that does not provide any actual insights. Making decisions based on such data can lead a business in the wrong direction and result in major setbacks in achieving strategic goals.
Solution. The success of any data project heavily relies on the quality and relevance of the used data. It is, therefore, unsurprising that nearly 80% of any such project should be spent on preprocessing and cleaning the data. This task is best dedicated to a team of experienced Data engineers who can do it quickly and efficiently.
Challenge 2. Lack of tools to collect and manage data.
Data-driven decision making relies heavily on the infrastructure and efficient data pipeline that is used to collect and analyze your data. Without proper tools, the process will be inefficient and will not yield the expected results.
Solution. A reliable data analytics partner can take over the entire development of your data delivery pipeline and ensure that you use the most efficient tools and technologies.
For example, during our partnership with a Fortune 500 company, the N-iX team was tasked with choosing the most fitting data warehouse technology. By performing a thorough analysis of Amazon Redshift and Snowflake, our team recommended using the latter as it provided more advantages based on the needs of our client. As a result, we helped develop a solution that was the most efficient in terms of both functionality and costs.
Challenge 3. Disjointed data sources.
Many organizations suffer from having multiple disjointed systems that collect data. In addition to not being standardized, the data is usually difficult or even impossible to access by those who need it. This directly impacts the data analysis process, which can become slow and result in misleading conclusions.
Solution. Having an efficient delivery pipeline that stores data in a single unified data lake will make sure that teams can quickly access and analyze the information they need.
Challenge 4. Lack of experience working with data
Without proper Big Data and Data science experts, a data project is doomed to fail. The fact that only approximately 15% of such projects succeed only serves as proof of this point.
Solution. This challenge can be avoided by partnering with a third-party service provider who can take care of both the technical implementation and the data analysis. If finding experts locally is difficult, you can always opt to outsource to a partner offshore. This will provide you with an additional range of benefits, from reduced costs to quicker development and access to a larger and more experienced talent pool. Eastern Europe, for example, has long been one of the most popular outsourcing destinations. It has a large pool of some of the best data experts in the world.
Challenge 5. Inability to scale the infrastructure
Data projects need a powerful and flexible infrastructure that can provide the necessary computing power to process an exponentially increasing amount of data. The inability to easily scale the infrastructure up or down leads to frequent downtimes and reduces the effectiveness of your data project.
Solution. When choosing a tech service provider for your upcoming data project, choose a partner with solid cloud development expertise. Migrating your infrastructure to the cloud will provide you with unprecedented scaling capabilities, as well as an almost limitless storage space and computing power. Moreover, cloud infrastructure is more cost-effective and easier to maintain than on-premise infrastructure.
How N-iX can help you with data-driven decision making (DDDM)?
- N-iX has a strong Data unit with over 130 data engineers that have successfully delivered over 30 projects;
- We offer a wide range of expertise that can support your data-driven decision making process, from Big Data and Data Analytics to Business Intelligence and Data Science;
- N-iX works with leading global enterprises from various industries, including FinTech, Healthcare, Telecom, Manufacturing, and more;
- We have over 20 years of experience in forming software development outsourcing partnerships;
- N-iX complies with all established data security standards such as GDPR, PCI DSS, ISO 9001, and more;
- We are the officially certified partners of all 3 leading cloud providers, being an AWS Advanced Consulting Partner, a Google Cloud Partner, and a Microsoft Partner;
- N-iX is among the top tech service providers, commonly found on various top lists from platforms such as Clutch.co and The Manifest.
