Why Success Rate Depends on Data Culture ?

`The Harvard Business Review Analytics Services, as sponsored by the AI-driven Analytics leader, ThoughtSpot, conducted a study titled, The New Decision Makers: Equipping Frontline Workers for Success, in 16 industry verticals across Europe, Asia-Pacific, and North America. As seen from the sentiments of around 464 business leaders, there is a direct link between empowering frontline employees for on-the-spot decision-making and organizational performance.

However, the irony of the current situation is that as much as 43% of organizations surveyed are Laggards as they fail to empower their workforce to meet present-day business needs. But what are the 20% of Leaders doing differently to drive success? Here's what we found -

• Providing intensive training

• Helping workers gain technological insights

• Prioritizing a data-driven culture

There are organizations where the top management discourages frontline employees from decision-making, where all investments are concentrated towards technology and data but not their integration, and a data-driven culture is not a priority. Such organizations' leaders need to step back and reconsider. The business' success rate depends upon a solid data-driven culture, and we discuss why in this blog.

How important is Data Culture to Organizational Success?

Growth Jockey believes that the simplest way to identify a data-driven organizational culture is to look at each department's cue for decision-making.

Does every member, from the CXOs to the entry-level professionals, work from a conviction that data can improve decision-making? If yes, this collective belief points towards a data-driven culture.

The opposite is true in the case of organizations where the mid-level management and employees are heavily dependent upon the top-level executives for decision-making. They lack the autonomy, resources, or insights to take quick decisions that may boost performance.

Multiple studies have concluded that organizations running on data-driven decision-making will likely see double-digit growth in record time, increased productivity, employee satisfaction, better operational efficiency, and exceptional customer service.

When businesses allow data and Advanced Data Analytics to direct their course, guesses and estimates become facts. This plays a massive role in getting all stakeholders on board and encourages employees to invest deeply in improving organizational growth. In a nutshell, data culture is a win-win for all!

What We Believe a Good Data-Driven Culture Involves

Many organizations, especially those new to the concept of data culture, tend to fall into the trap of data analysts and top-notch infrastructure. Though we believe these are important, the truth is that high-end CRM software and a group of skilled data analysts alone cannot create a solid data-driven culture.

This is because data-driven culture cannot be purchased; it has to be built from the ground up by marrying talent with training. A good data-driven culture stands on the bedrock of robust infrastructure, but its success boils down to three critical factors –

1. Data Discovery – Data analytics cannot succeed unless relevant data is available. The first factor is the data, which is easily accessible at the right time to draw insights.

2. Data Literacy – What is good data worth if employees do not know how to draw valuable insights? So, the next crucial factor is data literacy - the ability to analyze and interpret data for accurate decision-making.

3. Data Governance – After collection, proper data maintenance and management ensure the right people have access to relevant data at the time of need. Hence, data governance is the third and final key factor in a thriving data-driven business culture.

How Growth Jockey Builds a Solid Data-Driven Business Culture

The rate at which organizations build a solid data-driven business culture is the rate at which they grow. Growth Jockey employs the following strategies in building a thriving data culture.

1. Making Behaviors the Focus, Not the Culture

We firmly believe that excellence is not an act but a habit. So is the case with building a robust data culture. Jumping right into the cultural aspect seldom offers substantial changes. So, we direct our efforts on observing the habits and behaviours of team members toward data.

Ask employees what the most meaningful aspect of their work is, what roadblocks they generally face for efficient decision-making, etc. For instance – in some cases, we have discovered that negative employee attitudes were mainly due to past negative experiences.

It may also be possible that too much importance on innovative behaviours has led to workers treating the simplistic issues in an innovation vacuum. Having a grip on the organization's Data Pulse helps track or measure the extent of data-driven culture and how it grows over time.

2. Creating Team Awareness

More often than not, we've found that it is not data literacy that holds back organizations but a need for understanding data relevance. This is usually when advanced data analytics is not employed within the workflow.

Employees receive intense training on AI, ML, and other data-related technologies, but they need help seeing its impact. If AI and data analytics are incorporated as a part of everyday meaningful work, all members will understand data relevance.

One way we help bring this about is by using digital twins – a virtual model wherein a digital counterpart is used to analyze, monitor, and stimulate their physical twin. Through connected sensors, crucial information in the real world can be viewed, including equipment status in remote sites, product designs, and even the business model.

3. Building Detailed Metrics for Analysis

The more data analytics is employed, the more crucial it becomes to monitor and analyze outcomes. Without proper metrics, it's like shooting in the dark, not knowing where the organization's culture is headed.

Our team starts by setting small metrics in place. Gradually, we measure more complex behavioural changes toward data. Leaders or CXOs can also set an example by defining the most important metrics to measure. The rest of the company will soon follow suit.

Finally, we believe in steering clear of drowning people in data. Knowing which data belongs to whom and making it accessible accordingly is the way forward.

Red Flags to Be Aware Of

Growing an organization (through a robust data-driven culture) is a lot like nurturing a sapling. The growth is a green flag, but it's essential to beware of red flags such as diseased leaves, broken branches, damaged wood, etc., which call for pruning.

Similarly, employing more research in data culture and watching the organization grow will bring some red flags indicating a need for pruning. Some of them (along with methods to overcome them) are mentioned below.

1. Lack of Clear Vision for Analytics Programs

Sometimes, an organization's top management needs a clearer vision for Analytics programs. They may be aware of the importance of data analytics but need to distinguish between traditional analytics (reporting and business intelligence) and advanced analytics (which involves prescriptive and predictive Machine Learning).

When this is the case, even the most ambitious AI-pilot programs will lead to scepticism. If such an issue is identified, Growth Jockey's first line of action is to ensure everyone is on board. The new vision is communicated clearly from business development specialists to sales and marketing teams and beyond.

We encourage the CXO tasked with the program initiative to conduct workshops for executive teams so that they may understand the central tenets of Advanced Analytics and dispel any doubts or misconceptions.

2. Data Analytics Capabilities Isolated from the Organization

Businesses running on successful data-driven organizational cultures tend to embed analytics into their core business operations. If the data analysis tools create analytics capabilities in a siloed manner, it will lead to a significant disconnect.

Similarly, over-centralization will also develop bottlenecks. We believe the key is to have balance. So, what to do when a data scientist complains that their actions have no direct or tangible impact on the business?

Growth Jockey supports the development of a robust hybrid model, including professionals from both the analytics and business fronts. This way, specific capabilities are centralized whilst data analytics remains embedded into core business operations. There may be variations as a small team is more manageable centrally, whereas, over time, as teams become more efficient, centralization can be relaxed.

For organizations struggling with siloed data, Growth Jockey can help sail peacefully through a chaotic market. The team will help level the business using custom ops and tech solutions.

3. Negligence of Potential Social, Regulatory, and Ethical Implications of Analytics Programs

When considering how an organization gathers and analyzes data, it is essential to consider compromises. We believe in keeping ethical or regulatory issues at arm's length.

For instance – An organization building algorithms to understand the correlation between job conditions and worker absenteeism may develop bottlenecks. How? If the algorithms create employee groups based on gender, region, and ethnicity (despite such fields being off), the results may show a correlation between absenteeism and race.

To avoid such issues from affecting employee relations, we believe the company's CDO can take the lead in running a risk management program and conducting resiliency testing. This way, any problems can be identified and resolved at the earliest. Personnel can help the CXOs know the secondary effects of the analytics initiative.

The Way Forward

Ideas and hypotheses are great, but they will only retain stakeholder interest if supported by valuable insights and analytics. When an organization is led by research in data culture and data-driven decisions, moving forward in confidence becomes second nature.

Especially in the beginning, data-driven culture will seem complex (and it is). But we believe in starting with team members and their interactions with data, after which data analytics adoption can easily slide into the picture. Over time, new exchanges will shape future attitudes, blurring the lines between intuition and analytical decisions.

Though effective, the data culture model can take time to incorporate. But Growth Jockey also believes in the power of next-gen innovative marketing strategies and curated solutions for growth-oriented results.

When employed strategically, we can create a self-sustaining cycle of data-driven culture! Get in touch with us today to take scale your business with data driven strategies.