CULTURE – Let’s understand before transforming it

Company culture is a fundamental part of every organization’s functioning. However, a change in the culture becomes necessary for improving employee satisfaction and boosting productivity. Hence it becomes important to learn how to facilitate a cultural transformation in an organization to make sure that the employees are happy and working to the best of their ability.

Cultural transformation is much more than just a phrase. It is a commitment towards shifting the company’s culture so that employees feel like they belong here and can invest in the long-term success of the organization. When an employee feels this way, the company wins and workers who feel valued in their current positions are 50% more productive than those who do not.

What is Culture transformation?
Cultural transformation is defined as an evolving and developing process which changes based on the values defined by the organization This transformation rewards the organization with a thriving, values-driven company culture that drives sustainable success.

A cultural transformation requires taking a thoughtful look at your company and making changes to shape your policies, processes, commitments, and behaviours. A good cultural transformation will change the way the employees think, act, and deliver the services and products to your customers. Every interaction should reflect the cultural values starting from the first time a customer contacts the company to the end of the transaction.

A good culture within an organization will bring-in happier employees which are 20% more productive than unhappy employees.

Glassdoor’s research shows a positive relationship between customer and employee satisfaction. This link is even stronger in the industries where front-line employees have direct and frequent contact with clients. Hence, transforming the culture for improving employee satisfaction can have a significant impact on the customer’s happiness and productivity of the organization.

For understanding the real-life cultural transformation let us have a look at how Unilever Brazil did it.

In 2005, Uniliver’s revenues started to slow. To keep the momentum alive the company knew they had to make some changes. The company decided to make changes not just strategically, but they also made culture transformation within. The transformation relied on five changes: 

  • Accelerating growth
  • Creating sustainable transformation
  • Increasing revenue
  • Identifying capabilities required for boosting business.
  • Managing key performance indicators

The changes were strengthened by conducting cultural values assessments every six months. The result?

  • The Revenue grew by 3% in 2008 to 14% by 2010.
  • The company’s cultural score fell from 37% to 10%
  • The culture that was driven by internal competition, short-term focus transformed into a business environment with long-term goals, teamwork, shared vision along with an orientation towards customer satisfaction, and employee happiness.

Why data culture is important?
An employee usually spends over one-third of their lives at the workplace. Moreover, they ought to work for hours to meet the expectations of an organization. Naturally, when an employee is happy and satisfied at work, his overall personality and productivity is improved. When your organization ensures a good work environment, your employees wake up each day looking towards spending a great day at work. Hence the employee’s feel the same loyalty, ownership, and dedication towards the organization as you do, and the result clearly reflects in the work they deliver.

Without a strong data culture, organizations may be missing opportunities to use the data they have collected. A strong data culture encourages organizations to make reliable decisions which can be validated with the data. Hence the organization makes decisions by calculated risk which reduces the loss of revenue, growth, and people. Data in the organization can help them in becoming more efficient, which includes improving the customer experiences, building strategies, generating ROI, streamlining business processes.

How to build culture?
An organization’s customs, rituals, behavioural norms, traditions, symbols, and general way of doing things are the visible manifestation of its culture; they are seen when someone new walks in the organization. With time behavioural norms develop that are consistent with the organization’s values.

Though culture emerges naturally in most organizations, Once the culture is framed, the organization can establish a values committee that has a direct link to leadership. This group will make sure the desired culture is alive and well. Organizations must hire people who are aligned with the organization’s values and have the competency needed to perform the job.

How to sustain cultural capabilities?
The management of organizational culture starts by identifying the company’s culture traits. These traits are the core business activities, processes and philosophies which characterize how the organization does business day-to-day.

The culture can be managed by identification of these traits and accessing the importance with current business objectives. Leaders within an organization should approach culture management initially by gaining an understanding of the common traits found in all businesses. Then, they should take the following steps to manage their organization’s culture:

  • Start by Identifying common traits, including organization’s social, material, and ideological culture.
  • Representatives from all levels, functions and locations of the organization should assess the validity, significance, and monetary value of culture. (They must be assessed by shared assumptions, values and keeping in consideration beliefs of different individual’s)
  • Summarize findings and share them with all participants for additional insights.
  • Create a culture management action plan.

Practices to Develop Culture
When an organization does a good job in accessing its culture, it can then establish policies, programs and strategies that support and strengthen its core purpose and values. The same core beliefs motivate and unite everyone, cascading down from the C-suite to individual contributors.

There are many tools for developing an organizational culture which include hiring practices, onboarding efforts, recognition programs and performance management programs. The biggest challenge lies in deciding how to effectively use these tools and how to allocate resources appropriately.

Talent hiring and onboarding practices
Traditionally, hiring focuses primarily on an applicant’s skills, but when a hire’s personality also aligns with the organization’s culture, the employee is more likely to perform well. The unfit hires and rapid departures of unfit people cost approximately 50 percent to 150 percent of the position’s annual salary. According to some statistics nearly one in three new hired employees leaves within a year of hiring, and this number has been increasing steadily in recent years.

Some hiring practices which can be followed for ensuring cultural fit are mentioned below:

  • Ensuring that the organization’s vision, mission, and values statements are aligned.
  • Conducting a cultural fit interview.
  • Do not tell candidates about culture up front. Leave the discussion of company culture for later.
  • Make sure a minimum of three people is involved in the hiring process.
  • Onboarding is essential for introducing the newcomers with the organization’s value system, norms, and desired behaviours. Employers must help the newcomers in becoming a part of social networks within the organization and make sure that they have early job experiences that reinforce the culture.

Culture of rewards and recognition
These programs are used by employers to motivate employees to perform in accordance with the organization’s culture and values. For example, if teamwork is one of the core values, bonuses should value teamwork and not be based on individual performance. Employers should also put attention to those who personify the company’s values.

Employers must ensure that the organization clearly and consistently communicates its culture to all employees. Conflicting messages regarding corporate culture when heard may create distrust within people. Cultural inconsistencies will make workers discouraged, and they will not be able to perform the best.

Organizational culture needs continuous monitoring and assessment aligned with enterprise goals and objectives. Performing the below mentioned steps may help organizations assess culture:

Develop a cultural assessment instrument. This instrument will rate the members of the organization on the key cultural dimensions.

Administer the assessment. Survey respondents should include employees from all levels, functions, divisions, and geographical units of the organization.

Analyse and communicate about assessment results. Leaders and managers should discuss areas of agreement and disagreement about the culture.

Conduct employee focus groups. Just because top leadership agrees on organizational culture does not mean that all employees should see things the same way.

Cultural assessments, and other activities will help in identifying cultural inconsistencies. Leadership can then eliminate the inconsistencies.

Principles of culture
1. Align strategy with culture

Some corporate leaders struggle with cultural adoptions for years, without fully focusing on the question: Why do they want to change the culture? They are not able to connect their desired culture with the strategy and business objectives. Therefore, maintaining optimal balance between strategy and culture becomes important for enhancing productivity. For example, the Mayo Clinic which is world renowned for bringing together specialists from different medical fields to diagnose and treat complex diseases, the clinic promotes high levels of collaboration and teamwork.

2. Honour the Strengths of Your Existing Culture

It is easy to dwell on the negative traits of your culture, but corporate culture is a product of good intentions that evolved in unexpected ways and will have many strengths. They might include a deep commitment to customer service or a tendency of bringing innovation. If you can find ways to demonstrate the importance of the original values and share stories that illustrate why people believe in them, they can still serve your company well.

3. Integrate Formal and Informal Interventions

As the organization promotes new behaviours, making people aware of how they affect the company’s strategic performance, is important, be sure to integrate formal approaches like new rules, metrics, and incentives with informal interactions. Whether formal or informal, interventions it should do two things: reach people at an emotional level (how they feel about the work) and tap rational self-interest (providing monetary rewards, appraisals, and external recognition to those who come on-board).

4. Measure and Monitor Cultural Evolution

It is essential to measure and monitor cultural progress at each stage you make some changes. Rigorous measurement allows executives to identify changes, correct strategy and plan where needed, and demonstrate tangible evidence of improvement which can help to maintain positive momentum for long term improvements.

Cultural transformation in any company is a complex process. This culture should reflect what is important for them, and what is necessary for succeeding in their marketplace. What makes a culture transformation truly great is whether it enhances the talent and ability of its people. However, it can have a tremendous impact on the organization and its future. The effort is worth it, and it will be reflected in the employee satisfaction and productivity numbers in years to come.

Why would you tell a story with Data 01

Why would you tell a story with Data?

Why would you tell a story with Data?

What facts and stats are to the ears, stories are to the hearts.

Data Storytelling gives voice to your data. It is a way to express and communicate what data says in an efficient way. Storytelling creates a visual impact on the people. Data Storytelling creates magic on human’s brain.

If you want people to make the right decisions with data, you have to get in their head in a way they understand

-Miro Kazakoff Lecturer, MIT Sloan Tweet

As per Gartner, data storytelling will be the most widespread means of consuming analytics by 2025.

Why is data storytelling important?

Storytelling is an integral part of humanity. Stories play a vibrant role from the entertainment we consume to the experiences we share with others. Analysis of the most popular 500 TED Talk presentations found that stories made up at least 65% of their content.

Storytelling has proven to be a powerful delivery way for sharing insights and ideas with others. It is persuasive and engaging. Data Storytelling establishes the emotional connection with data and emotions plays an important role in helping our brains to understand things better. When we package up all the data insights with a story, we build the bridge for the data to influence the emotional side of the Brain. Humans remember the story and forget the statistics.

Data Storytelling merges three fields:

  1. Data Science: The field of science that extracts knowledge and insight from data, making it readily available.
  2. Visualizations: A way to transform data into visually appealing form like charts, graphs etc.
  3. Narrative: A narrative is a key to convey insights, with visualizations and data being important proof points. It is basically a way to give voice to your data.

Facts simply show data; whereas, a story’s narrative provides context, which provides better understanding and drives valuable insights.

Power of Data Storytelling

In today’s data-driven economy, data storytelling holds the critical “last step” of communication that conveys your insights into something that will inspire change and create value.

The art of storytelling has been used for communication for a long time. This art was completely ignored in data driven operations as it was considered a waste of time. However, over time, data storytelling has proved that, if data is not presented in the form of stories, most of the data ends up useless. Even if we have transformed insights from data and nobody can understand those insights. It is of no use. Which is why in most companies nowadays, the first step to analyzing data is storyboarding. To create this board, questions like ‘why are we analyzing this data’ and ‘what decisions do we hope to make with this data’ are asked. The aim is to start small because most of the time, data has the power to create visual and intricate stories that do not need complex correlations to make sense of it.

7 tips to construct a good Data Story

  1. Kick off with a question – Set up your story keeping in mind your audience.
  2. End the story with an insight – If the audience cannot learn something about data, there is no use of the story.
  3. Try to narrate a compelling story -Take the audience with you on your journey. Do not focus only on numbers, focus on Story.
  4. Explain story with Visuals and narrate story with words -People understand metrics, trends, and patterns with visuals rather than only words.
  5. Be authentic – Do not sugarcoat the negatives, try to be clear and honest for clarity.
  6. Be concise and clear – Do not stretch too much. Be clear and try to convey what you want to.
  7. Provide context -show metrics comparison overtime. Statistics and numbers are of no use without any context.

3 ways data storytelling can impact the business

    Turn Metrics into actionable insights – Data storytelling helps us to analyze the key performance indicators (KPIs) that align with the business goals and transforms quantitative data into result-driven narratives.
  1. Turn Metrics into actionable insights – Data storytelling helps us to analyze the key performance indicators (KPIs) that align with the business goals and transforms quantitative data into result-driven narratives.
  2. Improve Process with Plotting: Data stories have a definite structure with a well-constructed beginning, middle, and end. Data storytelling tools and templates
    Have well defined themes and formats which changes the visualization based on the input data and helps to narrate a story in the most effective way. Organizations can use predefined templates considering the primary aim of the data driven storytelling.
  3. Effective Communication and Engagement: It is very important for the organizations to maintain an engaging conversation with their clients and stakeholders for a longer time. To engage stakeholders and clients it is not just presenting the data, but we need to convey what data is speaking in a way that they understand. This way, they can better demonstrate the value of their services and build a lasting relationship with their clients and stakeholders.

Your organization must have a lot of raw data that can be transformed and used to generate insights, make useful business decisions. It is very important to communicate the insights. Data storytelling is the best tool to convey data insights with narrative. It adds emotions that can help audience to understand and engage with your statistics and data.


Data – by the systems, for the organization!

In the 2021 AI adoption survey conducted by O’Reilly, the biggest barrier to AI adoption is the lack of talent and the challenge in hiring skilled professionals. The demand of AI experts has clearly surpassed the supply in the industry, that is ambitious to build production-ready AI systems in next 1-3 years’ timeframe.

However, the companies still hold strong AI aspirations and to fulfill the same, they prefer the mix of talent to translate business needs into the systemic requirements, build AI systems, release, and activate by integrating AI into processes, and interpret results.

In 2017, Gartner said more than 40% of the AI tasks will be automated by 2020. However, the data and AI adopters see the value in automation as an opportunity to free up the human resource for creative tasks than using it for cut-down. The workforce with good business knowledge can play an important role in exploring data in the organization, provided they can handle data, and have a tool to explore.

Building the data democracy

Data democratization is the concept of making business data available to both technical and non-technical users in the organization. It allows users to access, manipulate, visualize, and derive insights from business data. To do this successfully, organizations ease the bottlenecks to provide seamless access to quality data within the data security and governance limits.

The state of data democracy catalyzes the consumption of data, that gives hands and feet to build data products at scale. Pharma research and development is one of the world’s most expensive processes, costing approximately $2.6B to get a drug through all stages of development and testing into the market. A pharmaceutical company headquartered in Germany, has created a data democratization process through which the metadata across various stages of clinical trials gets distributed on the shared framework.

Why is there a need to democratize data?

Nearly 72% of technology and business executives accept that they are trying hard to establish culture of data experience to open gates for data democracy. However, they do acknowledge the power of “open” data within the organization. More the number of people with diverse expertise accessing the data, faster would be the generation of business insights backed with quality data.

Furthermore, the process of picking a business problem, defining the success metrics, and integrating AI into the business process requires business teams and data scientists to join forces to design AI intervention. The reality though is – a group of data science experts get dumped with such requests from multiple groups and prioritization becomes a challenge. And acquiring domain expertise to understand the problem requires time, resilience, and perseverance.

What should a CDO do?

Growing culture of data within the business units is not an easy task. A clear and strong communication plays a vital role in valorizing data initiatives with the business functions. Here are some best practices how industry leaders have designed data democratization program in their organizations.

  1. Data advocacy boosts data experiments – In an organization, it is crucial for employees to realize the importance of data. Business users should be trained on the following:  problem solving skills, SQL skills and Exploratory Data Analysis. Training programs can help in upskilling users.
  2. Self-service data toolkit builds the capability- Self-service analytical tools help users to create reports and dashboards to get business insights in just a few clicks. It can be a great tool which can help many employees who are not very familiar with coding skills. It will save a lot of time as well. Investing in a self-service solution is a good plan to enable data democratization in organization.
  3. Data for everyone, but with governance: Ungoverned data access may become chaotic and build friction between the teams. A good idea could be data marketplace, where access to data sets pass through an authorization window.  
  4. Address data needs by personas – Organizations start with an assessment to survey different roles and capture patterns of data utilization. The objective is to define personas and build a data access framework. For example, a CXO layer needs a dashboard for storytelling with data and visual insights. Likewise, business analysts might need data models for analysis to advise based on insights.
  5. Define citizen roles and competencies – Citizen roles are pickup the pace in the industry. Non-tech folks with domain expertise can do wonders if they know how to read and interpret data. Typical citizen roles could be data advocates, analysts, or even citizen data scientists. These roles are expected to have strong data analysis, interpretation, and storytelling skills.

Into the future

Data democratization is an evolutionary trend that needs people to express themselves with data. It requires skills to handle data, then build interpretation, and finally take some actions. The CDO office supports these aspirations by structuring data literacy program, designing a robust data access framework, and position a low-code toolkit for “citizen” data community. While the data leadership in the industry continues to ponder on people and culture challenges, the democratization of data and AI is meant to scale up the numbers. The future of data experience is going to be interesting. Let us wait and watch!


Why does the Industry need a Chief Data Officer?

In 2002, Capital One appointed Cathryne Clay Doss as the Chief Data Officer. While the “computer science” industry was still gasping on the digital trends, a data officer was a role ahead of time. As the CDO, Doss led a wide range of strategic initiatives. But structurally, the role of CDO continues to be an ever evolving one. As per the analyst reports, until 2010, there were only 15 Chief Data Officers hired by different organizations.

Data technologies, science and processes are rewriting the rules of business and propelling organizations toward digital transformation.

A large amount of data that is being generated is very useful for organizations in order to use it for Business Intelligence. If data is cooked properly, then it can provide insights that can be helpful to increase productivity and revenue. Essentially, data can lead the way to new business models and result in fresh growth avenues.

Senior industry executives understand the importance of data and hence, they can be instrumental in providing strategic direction to the data landscape of an organization. Not only the data strategy, but their thought leadership can be crucial in rethinking business strategies with data. Their sense of ownership, authority, and influential attitude can smoothen the cultural transformation, resulting in data-driven practices. At a high level, CDOs portfolio is constituted and balanced around below subject areas –

  1. Data ownership and defense strategy – Governance, Security, Privacy, Quality, Cataloging, Master Data Management, etc.
  2. How to exploit data for business consumption? Data and Analytics
  3. How to build a value framework around data initiatives?
  4. What is the data monetization strategy?
  5. How to build new revenue streams with data?
Who is Chief Data Officer? A Chief Data Officer (CDO) defines the strategic landscape of data in an organization. The strategic landscape of data includes areas that strengthen the foundational elements of data, build and maintain the infrastructure for organizational data, ensure cross-cultural transformation with data, and value framework to measure the impact of data on business models. The foundational aspect of data covers various topics like data governance, security and privacy, data quality, cataloging, and master data management. This builds the data defense layer of the organization. The CDO is responsible for the vision and execution of Data and Analytics. The role is responsible for the development of the Data and Analytics department’s overall strategy and defining the business data management roadmap.

Why CDO is an ever-evolving role?
Organizations have been generating and accumulating data for a long time, but the volume of data started to grow exponentially in the second half of the 20th century. As per the analysis by Statista and Wikibon, the worldwide Big Data market revenues for software and services are projected to grow from $42B in 2018 to $103B in 2027, at a CAGR of 10.48%. Source: Wikibon and reported by Statista.

The corporates were very clear that with the growth of “data” comes the need of infrastructure, governance, and consumption. The role of Chief Data Officer was entrusted to spearhead these responsibilities only to realize that there are several other complexities associated with data initiatives.

The phase of “transformation” is not as straightforward as it sounds to be – it wasn’t a race, but a “marathon” journey. Every organization had their business models, vision and mission preambles, and above all, the culture that they have built over years. And these unforeseen and unprecedented challenges made the role of Chief Data Officer quite unsettled and an ever evolving one.

The New Vantage Partners survey reveals that 72% of its executive survey respondents feel the chief data officer’s role is still not settled while 28% describe it as a successful and established role. And by all means, it’s totally acceptable.  Large enterprises including Walmart, General Motors, Chase Bank, Bank of America, IBM, VISA, etc. have CDOs and this role is becoming increasingly common. While organizations are now adopting the chief data officer role, New Vantage says there is confusion on the importance of this position. Is it mandatory for organizations to have this role?

Once again, the New Vantage Partners survey shows that 40% respondents agree that the CDO role is important for building data-driven strategies and decision-making in organizations. The leading job portals (Indeed and LinkedIn) and executive search firms scout for the senior industry leaders across industries including Healthcare, Insurance, Media, Education etc. to fill the roles.

Since the first Chief Data Officer appointment made by Capital One in 2002 (followed by Yahoo in 2005), the count has increased to around 400 in 2014 and around 4000 in 2017. One of the key points that often invites a lot of arguments is CDOs position in the organizational chart. The “Chief” of CDO does play a role here – whether the data office should roll into CEO or sit as an advisor to the Chief Digital or Information officer. In either of the cases, the role acts as a trusted strategic partner and continues to rise as a key position at the CXO level. A 2018 survey by Gartner reflects how the industry has positioned the role of Chief Data Officers.

CDO Rolo is Well Established

Five years of data showing steady growth – all geographies and verticals.

For 2018:

  • 49% of CDOs reports to a top  business executive
  • 22% to a top IT executive
  • Stability in reporting relationships
Net out: Value of CDO role recognized – in terms of numbers and reporting relationships
So, what CDOs need to do? Amidst all the unsettling voices and variance in responsibilities, there are a standard set of items that naturally come to the role of the Chief Data Officer. Let’s go through the below list.
  1. Thought leadership – Build, transform, and maintain strategic view on internal as well as external data in the organization. Formulate data governance, security, and quality framework.
  2. Build the Culture of Data – Enable the use of data for better decision making. Roll out data literacy programs and data democratization initiatives to grow data practices.
  3. Grow data roles and communities – CDOs focus on building up data organization, advise new roles and competencies who can work with data effectively.
  4. Data & Analytics – Support data and analytics product development and delivery. This includes data architecture, infrastructure and engineering practices.
  5. Creates Value framework – Chief Data Officers are involved in the discussions across the organizational leadership to ideate the framework to measure the business value of data programs.
For the executives who are taking up the CDO role for the first time, quite often they are asked to lay down the microscopic and telescopic view of their activities. These views differ for internal transitions and external hires. But structurally, things start with a detailed assessment of organization, business models, IT systems, and competencies. This exercise builds the understanding of domain, current processes, and paints a view of what really needs to be done. Based on the study, the executives find the starting point, build the short- and long-term plan, build teams, and ensure early success for progressive endorsement of the data office. Don’t forget to check out the famous100-days CDO plan from Gartner.

No matter the role of Chief Data Officer finds intersections with C-level peers, the fact that strategic data leadership is vital to the organizations cannot be ignored. More than the technicians, CDOs provide thought leadership to structure the data landscape of the organization, act as transformational agents, and build the culture of data experience.


Democratization of Data Science

“This article was originally published on Analytics India Magazine

Introduction Chip Conley, head of global hospitality and strategy at Airbnb, believed that experience of staying in Airbnb is the soul of customer strategy. He accepted that data analytics is the key to understand and connect customer’s voice to Airbnb product line so envisage the experience customers are seeking when select Airbnb. In 2011, when Airbnb expanded out of SFO to 22 other locations internationally, they faced challenges with team’s mix, collaboration, and business transformation. Airbnb survived the challenge by building data driven culture and empowering its employees to work with data. Instead of building spoke teams, democratizing data practices helped them go faster in decision-making process.

Data democratization” means “liberating data” and “getting data in the hands of decision makers; it means putting data science tools in the hands of people who are not data scientists,

-Chris McGrath, SVP, Data strategy and consumer intelligence, Viacom. Tweet

Today, no industry is untouched from digital disruption and global economy is perceiving the business of data as one of the emerging sectors. What empowers Amazon to venture into healthcare? It’s the data that enable Netflix and Uber to transform business and build new markets. Per Forbes, data economy is expected to grow to 35 zettabytes and to analyze this huge heap, organizations needs analysts, if not scientists. The skills to analyze data and extract meaningful insights are still niche in the industry. Most of the organizations relegate data and analytics responsibilities to a central team. No issues with this equation, but the challenge is how to scale and stay sustainable in the long run.

The other major challenge is teaming and communication. Data scientists and business teams spend days in pointless discussions trying to dig through a problem. The lack of data literacy and ability to paint the business context causes brings friction between teams. This impacts business’s decision velocity and the ability to react on the fly, to internal or external factors.

What should organizations do?
The best way to bridge this gap is to establish data driven culture where everyone (required ones) understands data and possesses elementary knowledge on data driven outcomes. Democratization enables the sharing of tools, knowledge, and skills to consume data and back your decisions with data insights. In the current scenario, almost every industry holds large volumes of data and those who make sense of data succeed. While small central team of data and AI specialists bring standardization, democratizing tools and skills to generalists across the organization may bring empowerment and the ability to exploit data rapidly at scale.

Five-point strategy to democratize data science
Without data, there can’t be AI. To democratize data science, first step is to democratize data. If employees have access to data and the ability to explain it, all they need is a tool to build data models and judiciously apply data science techniques.Evangelize DATA – For business users to work with data, it is crucial for them to realize power of data. Business users must be educated on problem solving with data, intermediate level of SQL skills, and exploratory data analysis. Data training programs help in upskilling users at a much lower cost, but with a bigger impact. A collaboration workspace suite may act as a repository of FAQs, tutorials, and peer-to-peer discussions.

  1. Define PERSONAS – A persona is an employee’s context mapped to his access rights. Organizations usually start with an assessment to survey different roles and capture approaches to consume data. The objective is to define personas, understand what they look in data and build data access framework. For instance, CXO layer needs a dashboard for storytelling with data and visual insights. Likewise, business analysts might need data models for ad-hoc analysis to advise based on factual insights.
  2. Share TOOLKIT – Leave aside specialized AI tools, centralized data science teams can share tools that can connect to data systems, query data, run basic analytics, and generate insights. Data science specialists will no longer be burdened by ad-hoc data requests from the business. For standardization viewpoint though, organizations also position a self-service data platform – a low-code platform with drag and drop features, auto ML functions, and data viz capabilities.
  3. Build AI components – Democratization of data science starts with self-service data and matures with pertinent use of AI. Data scientists can publish generic pre-trained ML models that can integrate with other products. These packaged solutions enable rapid innovation, avoid rework by saving time and bring standardization.
  4. Transform CULTURE – Disruption tends to transform culture by challenging established processes. Democratization is one such disruption that is often perceived with reluctance and hesitation. Organizations must relay the larger vision with the employees for a better tomorrow.

Impact and challenges
Democratization of data helps in breaking silos and empowering business users when and where required. They need not engage with product or data science teams to explain and justify their case to bring up on priority. Data and the essence of AI enriches with questions. More eyes on the data, more questions and this could result in some unexplored opportunities.  

Business users, who might love to dig deeper into data, will emerge as citizen data scientists. Citizen roles in data analytics space have ability to think critically and interpret data science outcomes. Business teams with non-technical past must look forward to building this role internally for rapid inquires on data and analytics.

With all rewards being detailed, there are risks too with democratization. More users accessing data might put a question on data integrity and privacy. Data access framework should align with organization’s security and privacy norms. Confidential data can still exist in silos and doubly validated when requested.

The other challenge is how precisely one interprets the results of a data science exercise. AI results could appear inutile, if misinterpreted. Teams or individuals accessing data must understand the application of data science techniques and must know how to explicate the results. Focused training on data and data tools enables effective onboarding of employees.  

Data for everyone
For data democratization to be effective in VUCA world, organizations find that a centralized data strategy is imperative in breaking data silos into a unified data platform like data lake. Strong data governance in an ever-evolving data lake ecosystem guarantees data availability, keeps check on data quality, and brings consistency in architecture. Companies starting their democratization journey need to reflect into their digital strategy and evaluate data readiness. They can start small with few business teams, target quick wins, share tools and practices, and then go big with evangelization.


Decoding the recipe for Data-driven Culture

With the advent of the internet and technology, data has been growing at a tremendous scale and has become a pivotal factor in business decision-making. Organizations worldwide are adopting data analytics for their business growth, increasing customer experience, and employee satisfaction.

“Data will talk to you, if you’re willing to listen to it”
- Jim Bergeson

According to New Vantage Partners 2021 survey report, 96% (out of 85 Fortune 1000 firms) say their organization has benefited from Big Data and AI — up by more than 25% from 2020. Despite generating tremendous amounts of data, are the organizations able to utilize it completely to its full potential? Many of them accept that they are struggling because they lack culture of data.

Organizations produce huge amounts of data, but the ones who master the recipe of exploiting this data for themselves attain a competitive advantage in the industry.

Alan Duncan, Vice President Analyst, Gartner says, “The benefit of a data-driven culture is to examine and organize the data with the goal of better serving one organization’s customers and consumers. It also bolsters and speeds up business decision-making processes.”

Let’s define Data Culture
Data culture is the ability of an organization to use data for better decision-making. Companies with strong data culture rely on data for forming business strategies and in return business advancements. Imagine the world without data, where decision based on gut feeling and assumptions could adversely impact the business.

Data culture is not an option anymore; it is critical to the business

What’s new? Why Data Culture is important?
A data-driven culture embraces data consumption practices for decision making, it treats data as a strategic asset by making it accessible and available for the organization. The culture also catalyzes more frequent and frugal experimentation with data and unearth some fresh use cases for investigation.

Data-driven transformation can make organizations more efficient by enabling business strategies with data. For instance, a Voice of Customer analysis can help product companies to understand their customers better and address their expectation. Similarly, companies can build product launch strategies based on demographic data.

Internet giants like Google, Amazon, Netflix, and many others ensure all decisions are backed with metrics, data, and analytics. Be it board meetings or product discussions, the culture is to discuss questions with data and not the answers.

How to build data-driven culture
The data leadership responsible to transform data culture often struggles to get the right recipe. With an assumption that the organization aspires to be data-driven and has positioned a Chief Data Officer to build data-driven culture, let’s have a look at four simple techniques to kick off data driven journey.

 1. Shift the mindset – Changes are tough and depend a lot on the mindset. If the senior leadership of the organization do not believe in using the data, then it becomes difficult for other employees of the organization to adopt a data-driven mindset.

 How to shift the mindset?

 Leadership align on the use of data and act as influencers

  • Onboard business stakeholders on data-driven practices
  • Identify and communicate the business value of data

2. Strengthen the skill sets: In order to extract value from data, the organizational workforce must have the knowhow about the data, systems, and how to access. In addition, they must have knowledge to indulge in data exploration endeavors and interpret data insights.

How to strengthen the skillset?

 Upskill employees on data analysis and interpretation

  • Data Literacy programs to learn about organizational data, build community of practices, and cultivate the culture of collaboration.
  • Data Storytelling: The employees should learn the art of communication insight through data to the customers and stakeholders.

3. Selecting the right toolset: There are a variety of tools available for data management, data analytics, data visualization, etc. Every organization should understand the requirement and should select the right tools. Regardless of how many data systems an organization has there must be a common data language.

How to select the right toolset? 

  • We can automate some labor-intensive tasks such as data cleansing, exploratory data analysis, etc. by using automated libraries. So, we can align people in a more productive way.
  • By having an adequate amount of knowledge about the tools available in the market, an organization can select the optimal tool. 

4. Stronger data set: The relevance and the quality of data will determine whether it will be used to provide value in the organization or not. The organization must ensure that the data they consume is trusted, useful and secured. With data going out to multiple hands, a layer of strong data governance becomes a key ask that keeps a watch on data quality, authentication and authorization principles, and compliance on data exposition.   

According to a survey report of 2021 by New vantage Partners, where 85 data intensive firms participated, 81% of respondents have mentioned that they are optimistic about the future of Big Data and AI in their firms. 99% of firms have reported active investments in big data and AI and 91.9% have reported about the accelerating pace of investments. Below are some key insights –

 Only 24.4% have forged a data culture

  • Only 24.0% have created a data-driven organization.
  • Only 48.5% are driving innovation with data
  • Only 39.3% are managing data as a business asset
  • Only 30.0% have a well-articulated data strategy for their company
  • Only 29.2% are experiencing transformation business outcomes 

Data culture starts at the top and the leaders must be willing to invest and align expectations to use data in driving decisions. While it relies heavily on collaboration and trust, the decision-making priorities should align well with the data initiatives. Data-driven transformations essentially ask for change in habits, mindset, and even communication. It’s not a race, but a marathon. A culture that encourages data democracy, will enable the workforce on data-centric practices, make data-driven decisions and valorize data to kick off a longer-term growth.