Top 10 Sustainable Tips to Create a Data-Driven Culture
Increasing quantities of data have the potential to start a novel era of fact based innovation in businesses, bringing up new ideas with evidence. Organizations have amassed data and invested heavily in technology to enhance client satisfaction, streamline operations, and improve their business operations. Though for many businesses, a data-driven culture is far from reality, and data is not their base for business decision making.
Why is it difficult to do so? At Conneqtion Group , India’s 1st Oracle Paas Partners, our work in various industries show that the major issues to create data-based solutions are not only technical, but also cultural. It is easy to elaborate how to inject data in a decision making process. But, it is difficult to make this normal, for employees, because it needs a major shift in the mindset that offers a formidable challenge. Hence, we have compiled 10 data commands to help build a sustainable culture with data at its core.
Data-driven culture begins at the top of the Chain
Organizations with acute data-driven cultures generally have managers/executives who set a clear expectation that decision making should be based on data which is the ideal way to look at it. The management leads with an example of how it should be done and what they are expecting from the employees.?
For instance, at a large bank, executives go through the evidence from controlled market trials to make decisions on product launches. At a tech company, senior officials spend 30 minutes at the beginning of meetings to go through detailed reports and supporting facts to be able to make better decisions based on data.
These practices are followed by the employees, as employees need to communicate with senior management on their terms and in the language that they understand. The examples set by the management can go a long way.
Choose your metrics wisely
Strong leaders can make a major impact on behavior by simply choosing what to measure and what metrics they expect their staff to use. For example, think about a company that can profit by forecasting their competitor’s pricing. There is a metric for that: predictive accuracy through time. Hence, it is important for a team to make explicit predictions about the size and direction of these moves. By tracking these moves closely, they will certainly improve in the long run.
For instance, a major telecom company wanted to make sure that its network offers key customers the best in class user experience. However, they only collected aggregated data on network performance and hence had no idea about who was receiving it and the service quality they experienced. By building detailed metrics based on customer experience, the business could easily make a quantitative analysis of the consumer impact of the upgrades. To do this effectively, the business just required a much tighter grip on the consumption and source of data.?
A business can use the below performance metrics:
Marketing Metrics
Data-focussed marketing practices are one of the best ways to use data. A business should observe:
Sales Metrics
Data-centric sales are driven by data-focused marketing, though the metrics are entirely different. The sales team should focus on:
Customer Success Metrics
The importance of excellent customer success cannot be ignored. The ideal way to improve customer success is to include a data-driven approach in your current business practices. To ensure this, you need to keep a record of:
Management Metrics
It is possible to enhance management by including data-driven practices, as it is crucial for the management team. The following metrics can help your management:
Include your data scientists in the mix
Data scientists are generally brought into an organization, with the result that they and the management team knows little about. Analytics cannot help you provide any significant value if they operate separately from the rest of the business. Businesses who have done well have managed to address the problem in two ways.
The first step is to make boundaries between the organization and data scientists negligible. The objective is for the data scientist to work with the organization and not separately. More often than not, they will work on ways to merge domain knowledge and technical expertise.
Some businesses use another tactic. Instead of bringing the data scientist closer to the business, they try to take the organization close to the data scientist. They do this by ensuring that employees are code-savvy and fluent in quantitative topics. However, leaders of data-centric organizations cannot ignore the data language.
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Quickly resolve data-access challenges
The most basic complaint we come across is that employees in various parts of a business struggle to get their hands on the basic data. Interestingly, the situation prevails even after multiple efforts to democratize access of data in organizations. Due to lack of data, analysts cannot perform accurate analysis, and it’s impossible for a data-driven culture to thrive.
Most successful businesses use a basic strategy to solve this problem. Instead of sanctioning programs to reorganize their data slowly, they impart universal access to a few key metrics at a time. For instance, the data should be available department wise. By doing so, businesses can speed up the process of each individual department by allowing access to the data that they require the most.?
Quantify Uncertainty
Everyone knows that certainty is not guaranteed. Though most managers continue to trouble their teams for answers without a measure of confidence. They are missing a trick by doing so. When you ask teams to be quantitative about uncertainty, there are three major effects.
Firstly, it forces decision makers to tackle directly with potential uncertainty sources. Is the data reliable? Are there limited examples for a reliable model? How can these factors be included when there is no data for them? The solution: a process to keep the data fresh royally fixed the problem.
Secondly, analysts get a better understanding of their models when they have to evaluate uncertainty. By developing an early-warning system to take these trends into account and spot instances that would otherwise have been ignored. Hence, businesses can avoid losses due to a sudden rise in uncertainty.
Lastly, a focus on understanding uncertainty pushes businesses to run new experiments. This helps the business in the long run as controlled experiments of their ideas before making a major change helps them make an informed decision.
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Robust proofs of concept?
In data analytics, exciting ideas are more than practical ideas. More often than not, it becomes clear when businesses try to put proof of concept in production. A major insurance company organized an internal hackathon and crowned its winner. It was an amazing enhancement of a digital process only to discard the idea as it seemed to need costly changes to fundamental systems. Letting go of such great ideas can be detrimental for businesses.?
An ideal option is to engineer proofs of concept where a primary part of the concept is the viability in production. A great way to start something is industrial grade but simple, and later increase the level of sophistication. For instance, to enforce new risk models on a large, distributed computing system, a data products company began implementing a basic process that worked end-to-end. This helped move a small dataset correctly from source systems and via a simple model and was transmitted to customers. Once that is sorted, the organization can work on improving each component separately: bigger data volumes, more exotic models, and enhanced runtime performance.
Specialized Training
Most businesses invest in high-cost training efforts, only for the staff to quickly forget what they have learned if it is not used practically in time. Hence, basic training like coding must be a part of fundamental training, it is more efficient to train staff in specialized analytical concepts and tooling right before these are required as a proof of concept. One retail giant waited till a market trial before training the employees and got the benefits of timely training. The knowledge offered came right in time, and once-foregin concepts were now a second nature of the employees’ vernacular. So, specialized training will always help employees to perform to the best of their abilities and get a better ROI for the business.
Use of Analytics for Employees
It is easy to ignore the potential role of data fluency in enhancing employee satisfaction. Though empowering employees to manage data themselves can do this, as it allows them to perform better.?
If the concept of learning new skills to better manage data is showcased in the abstract, few employees will be happy to work hard and redesign their work. However, if the immediate goals directly excite the employees by saving time, avoiding rework, or collecting frequently required data then a task becomes a choice.?
Many years ago, the analytics team at an insurance company self-learned the fundamentals of cloud computing so they could experiment with the latest models on large data sets without waiting for the IT department to catch up with their requirements. The experience worked wonders for them, IT remade the business’s technical infrastructure. As the time came to draw the platform requirements for further analytics, the team was well equipped for the task and they could offer a working prototype.?
Trade Flexibility for Consistency
Many organizations that rely on data have various “data tribes”. Each data tribe may have its own desirable sources of information, custom metrics, and favorite programming languages. This can be a disaster waiting to explode across the organization. Businesses may waste hundreds of hours simply trying to settle multiple versions of a metric that could be universal to begin with.?
Inconsistencies in how modelers do their work takes a heavy toll too. If there are different coding standards across the same business, every move by analytical teams to retain it will make it difficult for them to circulate.?
It can also be complicated to share ideas internally if they need translation. Businesses should pick canonical metrics and programming languages. A major global bank took this route, by ensuring that the new employees in investment banking and asset management knew how to code in Python.?
Explain Analytical choices better
For most analytical challenges, there’s often a single, right approach. Instead, data scientists must make choices with different tradeoffs. Hence, it is better to ask teams how they tackle a challenge, what alternatives they worked on, what they identified the tradeoffs to be, and why they chose an approach over another.?
By doing so, teams get a better understanding of the approaches and often prompt them to consider a wider range of solutions. A global financial services business at first believed that a conventional machine learning model to detect fraud could not run fast enough to be used in production. However, they later understood that the model could be better with a few basic tweaks. When the business started leveraging the model, it encountered amazing accuracy in fraud detection.?
Conclusion
Businesses - departments as well as employees that comprise them - often fall back on habit as it is too risky to consider alternatives. Data can offer a type of evidence to back up hypotheses, providing managers the confidence to jump in new areas and processes without taking a leap of faith. However, just wanting to be data driven is not enough in today’s competitive market. To be data driven, businesses need to build cultures where this mindset can thrive. Business leaders need to promote this shift with an example, by forming new habits and encouraging their employees to follow suit. What do you think about including a data-driven culture at your organization? What are the challenges that you currently face or may face in the future? You can get in touch with us for more details and we would be happy to assist.?