This article dives into the challenges faced by industry experts in integrating data and analytics into their work streams, and how Kausa fits into the equation to mitigate the resulting effects of the aforementioned challenges and empower data-driven decision making.

With the world slowly heading towards a digital revolution, the importance of data and analytics is also consequently becoming increasingly rampant. Data-driven is no longer the goal, it is the requirement. However, the desire for this transition alone does not make the integration any easier. Despite a universal agreement on the power data-backed decisions hold, many companies are still struggling to make that a reality and reap the benefits.

According to a study by New Vantage Partners, only 31% of executives feel they have been able to build a “data-driven culture.” And according to Gartner, 87% of companies have low BI and analytics maturity. Experts in the industry have reported multiple challenges that they have faced in trying to embrace a data-driven culture, which is summarised below in 3 broad categories:

 

1) Scaling Data Analytics

As organisations grow, so does the amount of data they collect, which makes analytics increasingly difficult to navigate. It is close to impossible to increase the resources allocated to data (budget, team, hardware, etc.) at the same rate as the volume of data increases. A major problem area around scaling the use of Data & Analytics is also underestimating the security implications. Substantial amounts of diverse data are usually in one place, which creates potentially new vulnerabilities and represents new types of business risks to mitigate.

Tools like AI and ML can do remarkable things with a lot of data, but to scale analytics the two key questions are "do we have the right data?" and "are we exploiting the data in the best way possible?" Answering yes to both of these is indeed a challenge most organisations are working to resolve. The real problem occurs when you try to combine data across various sources. For instance, combining sales data with marketing, or supply chain data with sales; that’s when the cat comes out of the bag. Though these IT systems for Sales, Marketing etc., in isolation work like a charm, when you try to build pipelines to combine them Data Quality/Integrity issues hamper your progress to scale analytics.

 

2) Developing data and analytics-driven culture, literacy, and employee behaviour

What does it really mean to have a truly "data-centric culture?" This means that every single person - irrespective of expertise, tenure, or department - should be enabled to make better decisions based on data. This is something that is seldom found on the company level, although everyone seems to be wanting this. So the question that begs: what is really stopping them from achieving that data-driven culture status? This can be broadly categorised as follows:

a) Inaccessible data

In the quest of becoming data-driven it is not only important for each individual to have the necessary toolkit to deal with data, but also what data they have available and how that data can influence decision making. The quality of data most companies possess is rarely up to the mark that it can be used in ways desired. As more decisions are augmented by data, organisations must consider where these decisions are taking place and reduce the frustrating amount of time spent moving between these applications.

b) Apprehension Over Changing Roles & Skills

According to a study conducted by INC, neuroscience has shown that uncertainty feels similar to failure in our brains. That’s why so many people would rather avoid organisational change because of how uncomfortable the process of learning something unfamiliar can be. As the importance of analytics begins to seep through the foundation of an organisation, some teams may find themselves caught off guard, scrambling to understand how their skills transfer, if they do at all. Executives must not be hyper-focused on creating a data-centric culture to the extent that they do not associate any human element with it. They must think beyond policies, systems, and organisational charts to consider the psychological elements behind developing a data-first atmosphere.

In addition, to break long-held habits and encourage the use of data beyond a core group of analysts, organisations should invest in educating their entire workforce, irrespective of the department they work in, to become more data-literate and explore ways to not only create a streamlined experience for newer users but also find cross-functional common ground between departments, supplemented by data.

 

3) Monetising data and analytics, and treating them as assets to maximise economic value

It is never enough to simply have data. The value of data comes from the insights it generates, the processes it optimises, and its ability to enable better decision-making. The reality is, despite data and analytics hype and expectations, most organisations are not successfully monetising their data. It’s not uncommon for organisations to keep investing in systems even when they don’t produce the benefits they had promised. The real challenge comes in generating value in the form of increased sales, revenue, installs etc from the aforementioned data. Many organisations look into making significant investments in data assets, but without the appropriate strategy, they run the risk of putting the cart before the horse and making poor and regressive investment decisions.

 

Kausa & Data Analytics

Having the right set of tools can, at the very least, facilitate mitigating the effects of the aforementioned challenges. Kausa is key on your road to become a data-driven company by enabling anyone to run an analysis with a few mouse clicks, and no technical expertise required. Ad-hoc requests can easily be answered by adding one filter or just drilling down on a specific subgroup.

Kausa's tool will do all the manual work for you, without missing any corners or making errors humans may be prone to do. You can easily delegate the heavy lifting to Kausa, which hypothesis-tests all factors to find the reasons why your metrics change. So you won't have to worry about how to make the best use of the data, and just focus on the insights Kausa has already generated for you. These all-encompassing insights then translate into what you and your organisation essentially work towards - maximised economic value in the form of increased installs, revenue, or margins.

Whether you have employees with no technical knowledge feeling apprehensive towards a data transition, or experienced analysts looking for more time on their plate to be able to focus on more strategic projects, Kausa will have you covered. Its collaborative features allow users to easily share one analysis link on Slack or simply tag a colleague and comment. This way multiple teams can provide their inputs and business expertise on a central insights platform, fostering a more modern data culture and data-driven decision making.

 

Conclusion

While Kausa does not promise to transform the industry (yet) and solve every challenge your organisation faces, it can certainly facilitate in creating the data-driven culture you have been working towards creating. So you won't have to worry about your employees feeling alienated and not knowing the data's applicability across different departments.

Interested in trying out Kausa? Get in touch with us.

Kausa accelerates data exploration, delivering actionable insights in seconds by testing all hypotheses comprehensively and continuously.

About the authors and Kausa

Usman Amjad is the Business Development Executive and João Sousa is the Customer Success Manager at Kausa.

Kausa accelerates data exploration, delivering actionable insights in seconds by testing all hypotheses comprehensively and continuously.