02.08.2022 | admin
How to Run Airbyte and Get the Most Value of the Facebook API
Companies are investing trillions to become data-driven, but few see the payoff. A McKinsey survey of 1,000 companies (with more than $1 billion in revenue, spanning 13 sectors and 12 geographies) showed that only 8% of the companies succeeded in scaling their data analytics. That is, only a fraction of billions invested was returned.
How do you make sure the resources poured into data don’t go to waste? Focus on the right practices.
For this article, we’ve used this and plenty of other research and combined the findings with our experience providing analytical services. And we came up with five stages of the data analysis process and five ways to grow your business value with it.
There is no point in generating data visualizations if you don’t know why you need them. Similarly, focusing on the wrong metrics – like satisfaction survey scores – makes for ineffective prioritizing.
We suggest you move forward with data analytics in a way that makes sense for your business needs.
Data is only as good as the questions you ask. No matter how advanced your tech stack will be, you won’t get ready–made solutions unless there is something specific you ask from it. This is sometimes called a “problem statement”.
At this point, you need to translate a business objective into a definitive goal. For example, for the next six months, your main business goals may be to:
Then, you need to create a list of questions data will provide answers to. In this scenario, it may be the following questions:
Most analytics tools tend to fall into a few key groups:
You can focus on one type or combine several ones, depending on the goals established earlier.
If you try to incorporate every possible metric, you’ll likely find yourself in data bloat. That will obfuscate important data and increase time spent on data management and analysis. The opposite can also cause a problem. Too few metrics will offer an incomplete picture, which will make it hard to make significant improvements. That would be as good as going in blindly.
Fundamentally, your choice of metric should be driven by the two previous steps – the goals and the tools.
Keep in mind that as times change, your goals will too. Therefore, the list of metrics should be revised as the organizations’ needs change. Some basic metrics will be evergreen (sales revenue, customer acquisition cost, customer retention rate, etc.). Others, which were once worthwhile (runway, burn rate, etc.), will no longer help meet your growth goals.
The type of data visualization technique will largely depend on the type of data chosen to be modeled and its intention. Of course, the bigger goal is to communicate information clearly and efficiently by presenting data in a graphical format. But the smaller goals will guide the type of visuals used (charts, tables, maps, infographics, etc.).
Depending on who’s performing the task and their level of technological proficiency, you’ll get visualizations ranging from most simplistic to detailed and complex. Here is an example of the latter.
By themselves, data visualizations don’t offer any insights. They are the tools to deliver information to stakeholders or teams who will interpret it and draw conclusions.
Finally, you get answers to your questions. But there are also a few choices to be made – how to interpret the results.
Here are some of the categories, from least to most complex:
As the level of complexity and effort of data evaluation increases, so does the value of findings for the organization. You can also look into exploratory analysis, which studies the relationship between the data and the variables to spot patterns and anomalies, test hypotheses, and check assumptions.
Here are five ways you can derive value from data analytics for priority use cases.
Historically, companies would identify market opportunities from obvious trends, which is a practice with a healthy dose of trial and error. Resources would be poured based on a few basic datasets, such as the historical and current performances.
Now, it’s not the past existing market trends that inform what a business should do. Predictive and prescriptive analysis can highlight these opportunities before they come, shifting the focus from the human element and educated guesses to data-based insights. For example, before expanding the business into new markets, you’ll see relevant data regarding the gaps, demand for your product, and the competitive landscape.
Transforming the decision-making process in a company is no easy task. But if you commit to incorporating data and analytics into these cycles, you will see the transformative effect even before the decision starts to bear fruit.
With a data-driven approach, you’ll find it easier to reach a confident decision about most business challenges. This is because you’ll be able to better understand the impact of your decisions. The approach removes the subjective elements, which allows decision makers to not be overly concerned that the wrong decision has been made. The decisions won’t always be correct, but they’ll be measured and monitored to ensure a better decision next time.
One thing that can help you arrive at the right decision is context-driven analytics created from modular components. In other words, different teams can have their own subset of data and make their conclusions. A decision maker then consolidates these isolated building blocks.
By analyzing the last month, six months, or beyond, you can understand each business component individually and within context. Each activity can be held accountable for the results it brings. If the results are subpar, the data will show it, and you won’t pour resources into the strategies that don’t work.
You can also optimize your processes with other companies’ data – through data-sharing arrangements with external partners and competitors. According to McKinsey, companies that participate in a data economy that enables the exchange, sharing, and supplementation of data will be able to create better insights. For example, partners will pool their respective data so that each company can generate value that is “much greater than the sum of its parts”.
In another paper, McKinsey points out that simply asking customers about their experiences is becoming obsolete. Survey-based measurement systems are limited, reactive, ambiguous, and unfocused, whereas data about customer interactions can be used to accurately predict both satisfaction and the likelihood of someone becoming a loyal client.
Let’s focus on the reactive vs. proactive approaches to customer problems. McKinsey’s research shows numerous examples where companies that boost their capabilities can identify issues and opportunities in real time. Even more, they start anticipating behaviors and even preempting problems in customer journeys, from getting stocked up on a product to compensating for a delay. The use cases span beyond a typical customer – it also applies to clients, guests, patients, members, and intermediaries.
The financial benefits come as a result – predictive customer behavior systems can have a direct impact on metrics like cost-to-serve, cross-sell, and up-sell behaviors.
Analytical and predictive modeling techniques can be inclined towards major areas of business operations, such as below.:
Proactive planning is not a novel practice, but efficient use of real-time data helps organizations shift from annual forecasts to rapid strategic modeling. The last few years have shown that unexpected disruptions can turn everyone’s predictions on their head. And in times of uncertainty, data analytics teams need to deliver business insights quickly and enable leaders to make sound decisions.
KPMG suggests the following practices for continuous forecasting:
On a final note, we’d like to highlight the importance of cultural change within an organization. While you might think that tech integration is key to becoming a data-driven organization, there is a strong consensus that organizational alignment is more important to support business objectives.
“For the 4th consecutive year, over 90% of executives point to culture as the greatest impediment to [becoming data-driven]. Only 8.1% cite technology limitations as the primary impediment.” —New Vantage Partners, Data and AI Leadership Executive Survey 2022
As organizations look to the future with the aspiration of using data analytics for growth, they should keep in mind that fundamental changes may take time.
For organizations, it can be incredibly helpful to strategically partner with data analytics experts to make it a worthwhile investment. With the right goals, tools, and practices, companies will be able to gain a competitive advantage and an overall reduction in cost because fewer “mistakes” will be made.
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