Data Modelling

Large enterprises have a lot working to their advantage – large budgets, a huge global workforce, global supply chains, and salesforce…the works. For a startup, these factors can look intimidating. But it pays to remember that every global giant was once a small fish in a big pond. It is by taking advantage of their strengths that they became the behemoths of today. The startup narrative has also undergone a sea of change over the years, with some very smart solutions coming up from this space. And large enterprises looking for solutions have noticed this. Here are some compelling reasons why some enterprises are taking a keen interest in working with startups to meet their needs. Agility Agility is perhaps one of the greatest advantages of a startup environment. Today businesses are evolving. Markets are changing and experiencing a state of constant disruption. Business models are in a state of constant evolution. Customer demands and expectations are becoming increasingly fluid. Technology changes and advancements are demanding organizations to come up with new solutions continuously. Given the smaller setup, startups gain an advantage here as it helps them remain more nimble and agile to change. What is becoming clear in today’s age is that success does not necessarily come from the position, scale, or first-order capabilities. Rather it comes from ‘second-order’ capabilities…capabilities that allow organizations to foster rapid adoption and the capacity to act on the signals of change. This is where startups score big as their internal structure allows them to become more responsive to change, come up with creative solutions to pressing problems faster and hence deliver what the customers want, when they want it. Greater risk-taking capabilities Startups have greater risk-taking capabilities than their larger counterparts. This is primarily because there is no bureaucratic red tape to navigate to implement change. Since the needs of the customer are at the heart of the startup culture, it is their needs that dictate the risks the startup needs to take. Initiating a change process, altering a roadmap, changing technology to meet the needs of a product, etc. are much easier and faster in a startup set-up because of the absence of slow-moving decision hierarchies. Access to the latest technologies and trends Technology startups usually work with the latest, and some of the most trending technologies. Their market positioning also demands them to stay updated on the latest technology trends. Want to know which direction UI and UX are heading? Ask a startup. The resource pool of technology experts working in startups is also adept at coming up with creative solutions to pressing problems using the latest and the most relevant technology stack. Large organizations can gain access to qualified and trained professionals without incurring the cost and expending the time and the effort to locate and hire a trained resource by simply working with a startup. This becomes even more relevant as technologies such as AI and Machine Learning start becoming more mainstream, and accessing top talent becomes harder. Since most startups work in a niche area, they work with niche technologists to develop robust and relevant solutions to suit market demands. Rapid prototyping The early stages of designing technology solutions demand the capability to build a working prototype to go-to-market faster. Rapid prototyping is much easier in a startup environment because of a short feedback loop. Along with this, startups don’t have complicated, interconnected, and rigid tech stacks. With clearer communication between stakeholders, access to the latest technologies, low technical debt, and a willingness to come up with a compelling solution, startups become more adept at addressing stakeholder engagement, client demands, retrospectives, and build smart alignments that contribute to rapid prototyping. It is these same capabilities that make it easier for startups to make greater customizations for their customers. Feedback-driven When change is the only constant, it becomes imperative to be open to feedback and have the velocity to implement it. Changing product requirements are a given today – stakeholders can rethink the requirements and features. End-users might demand new features and functionalities. The technology choice might need an overhaul keeping business evolution in mind. New elements might need to be introduced to make the product more attractive and useful. Startups are adept at incorporating all the feedback owing to the absence of bureaucracy and structural flexibility because of smaller, tighter-knit teams. This helps them make group decisions based on feedback faster and implement change without impacting the velocity of development. Today, collaboration has moved from becoming a buzzword to a business imperative. It has become essential for innovation. Organizations that enable collaboration are successful. Those who don’t have to implement it eventually. A similar collaboration between large enterprises and technology startups can be the key to foster innovation across geographies and benefit both sides – the large enterprises to create and enter new markets and the startups to develop their solutions and to scale. It’s a win-win for both.

According to Gartner, the primary job function of citizen data scientists is predominantly outside the field of statistics and analytics. However, they create or generate models that leverage predictive or prescriptive analytics. The profile name “citizen data scientist” bears a close association with “citizen science,” where the findings from the general public contribute to the scientific research carried out by professional scientists.  National Geographic’s Citizen Science Projects are an excellent example of the same. In the business world, citizen data scientists today play a complementary function to professional data scientists. Traditional data scientists’ professional knowledge is frequently expensive and hard to come by. Citizen data scientists are effective assets to close the present skills gap.  This article serves as a guide to CXOs and covers how citizen data scientists and data science teams should be more collaborative than competitive.  The Need for Collaboration Between Citizen Data Scientists and Data Science Teams  Although the practice of citizen data science is growing in popularity, this collaboration is rarely considered or addressed. However, this needs to change to nurture more collaboration among individuals working to drill down into data and unearth actionable insights.  Here’s why the need for collaboration between citizen data scientists and data science teams is highly reasonable. Fostering a Collaborative Data Science Ecosystem Data science training for non-data scientists is an essential step in any company’s journey towards: Nurturing data science competency within the organization Acquiring a greater understanding of the current gaps in the offerings, market, workflows, etc. Increasing transparency and trust among people working on data Advancing the democratization of data science and facilitating equal opportunities for all stakeholders to contribute to decision-making Related Reading: The Democratization of AI and Machine Learning: Making Advanced Analytics Accessible Citizen data scientists need to work hand-in-hand with professional data scientists to ensure that relevant insights are churned out from the entire stack. Such an ecosystem would spawn more innovative solutions and processes while ensuring that quality data is quickly made available to the entire team.  In such an ecosystem, CXOs would be able to have a greater say in the formulation of goals, and they would be able to order more precisely the resources needed from vendors and other teams. For this, however, it’s critical that data science roles are clearly defined. In case of discrepancy, accountability Data scientists must be aware and open to collaboration with citizen data scientists at all levels of the organization. Reaping Maximum Benefits from Augmented Analytics Extending the capabilities of the analytics initiatives is something that CXOs aim for to accommodate a holistic and more profound view of the business functions, especially as the business scales and evolves.  With citizen data scientists on board, organizations can lay out a concrete augmented analytics roadmap that follows a phased approach to creating a holistic data science ecosystem.  For example, let’s assume that citizen data scientists carry out activities, including fundamental reporting, exploratory analysis, and data curation. The enterprise wants to augment these capabilities with functions that facilitate data storytelling, data querying, etc. Instead of opting for a big bang approach where they completely change the toolkit and processes to accommodate the next set of capabilities, it’s more lucrative for CXOs to implement a phased approach where citizen data scientists are equipped with enhanced resources to perform advanced analytics.  Driving Collaborative Model Development and Deployment Citizen data scientists can collect data and ensure that it is accurate and indicative of the target business environment. They can outline the pertinent characteristics, factors, and practical limitations affecting the problem domain. Data scientists can handle complex analytical problems and guarantee that the models follow the best standards. They can help with algorithm selection, model parameter adjustment, and the use of strict validation approaches like cross-validation and out-of-sample testing. This collaborative approach ensures that the predictive or prescriptive models are accurate, efficient, and in line with the unique needs and business objectives. This, in turn, results in better decision-making and more effective model deployment throughout the organization. Enhancing Business Resilience Often, businesses find themselves in the peculiar situation of not having enough expert data scientists and having to implement innovative solutions with limited resources.  Whether it’s because of talent shortage, lack of skillsets, or simply the fact that a data science team is still in its nascent stages of development, it’s common for enterprises to have a dearth of in-house data scientists to address critical business problems.  In such situations, it’s incumbent upon the CXOs to find a way in which they can utilize the expertise of their small data science teams — something that becomes even more critical in a scenario like the economic downturn of today.  This is where they can look toward skilled information analysts in their teams. These professionals might not have an out-and-out data science degree, but they have in-depth knowledge of statistical modeling and forecasting.  When equipped with AI, NLP, and ML tools, these analysts complement the work of the data science team.  Role of a CXO in Facilitating Collaboration Between Citizen Data Scientists and Data Science Teams CXOs should be aware of the difference between an information analyst and a data scientist. They should have a clear understanding of the right mix of talent needed for the business, as well as have a sense of how their team can best utilize the data science stack to further develop their capabilities. A clear vision and plan must be laid out by the CXOs for data-driven activities across the company. This includes: Underlining the significance of collaboration between citizen data scientists and data science teams Cultivating a culture that encourages cooperation Dismantling team silos and fostering information sharing Equipping citizen data scientists with the right resources and technology infrastructure Positioning citizen data scientists for greater accountability, responsibility, and authority Providing mentorship and training to expose them to cutting-edge tools and techniques It’s here that they can immensely benefit from a unified data science platform like Rubiscape.  Contact us to learn more.

As more companies become data-driven and with global data volume expected to grow to more than 180 zettabytes by 2025, the need to go beyond traditional analytics has become critical.   Gartner defines advanced analytics as using sophisticated tools and techniques to unlock deeper insights, make predictions, and generate recommendations. It is more effective than traditional business intelligence.    Companies use techniques like data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, graph analysis, simulation, complex event processing, and neural networks for advanced analytics.    Advanced analytics can solve various problems across various industries, such as finance, retail, and manufacturing. Financial institutions, for instance, can use advanced analytics to detect fraudulent activities in advance and reduce risks. Manufacturing companies can use it to spot quality issues and fluctuations, while retail companies can use it to forecast demands, spot trends, and manage inventory efficiently.   From forecasting trends and issues to prescribing solutions to improve business outcomes, advanced analytics can help companies to meet customer demands and grow business. In fact, advanced analytics is the foundation for Artificial Intelligence (AI) and Machine Learning (ML) initiatives.   But some limitations stop companies from using advanced analytics. Let’s look at them and find out how democratizing analytics can solve this problem.    Challenges of Advanced Analytics 1. Large Data Volume The data teams often grapple with the sheer volume of data they receive every day from different touchpoints. Almost 80% of their time is spent on data cleaning, and a mere 20% is spent analyzing it. As the data volume increases, the data teams cannot keep pace and deliver real-time insights to other teams. This delays the decision-making process.   2. Skills Gap According to a survey, 74% of decision-makers in the data and analytics industry admitted to a talent shortage. By 2025, the US alone will face a shortfall of 250,000 data scientists. This talent shortage prevents companies from implementing advanced analytics or undertaking new AI and ML projects.   3. Lack of SMEs Advanced analytics requires specialists who can interpret the data correctly and recommend insights. It requires multi-disciplinary collaboration between different subject matter experts (SMEs). However, silos between different teams make the implementation difficult.    4. Poor Data Quality The data team receives poor-quality data from various sources that are inaccurate and incomplete and faces data latency challenges. This impacts the datasets and could lead the team to build a flawed data model affecting business outcomes.    With the volume of data increasing each day, the data team would require more data scientists to keep pace and interpret data correctly. Without that, companies cannot harness the full potential of advanced analytics or build successful AI and ML models.    Democratization of analytics can help companies overcome these challenges and deliver a positive customer experience.    How Can Democratizing Analytics Help? Analytics democratization is the process of making data analytics accessible to more users in the company. With the help of advanced tools and technologies, companies can remove the barrier to data and provide self-service capabilities to users to understand, interpret, and visualize data and make decisions without technical knowledge. This can empower users to develop AI-based solutions and fuel the company’s innovation.    1. Mitigate the Talent Shortage Problem Currently, it takes 45 days on average for companies to fill data analytics jobs. The talent shortage and the lengthy hiring process lead to unnecessary project delays and cost escalations. Given the scarcity of specialists in this field and their ever-growing demand, companies have started democratizing advanced analytics.    The non-technical business users from diverse disciplines can use AI/ML-based analytics tools to derive insights from data and make decisions. Anybody from marketing and sales to human resources and finance can use it without involving a steep learning curve.    2. More Innovation Continuous innovation is paramount for companies to thrive in a hyper-competitive environment. Despite having access to large data volumes, the data remains untapped. That’s because the data is inaccessible to other teams. It hinders the development of groundbreaking solutions.    With advanced analytics democratization, companies can grant data access to all teams and empower them to drive innovation. This transformative capability is vital to unlocking growth and propelling companies toward success.   3. Faster Decision-Making In a fast-paced business environment, companies must be fast in spotting trends, forecasting demands, and meeting customer expectations. They cannot rely on the data team alone to analyze and deliver insights.    Additionally, every department has unique business requirements. For example, the finance team might need AI/ML-based solutions to understand the company’s financial health, while a supply chain department would need it to improve inventory accuracy or calculate average delivery time based on the distance to be covered.    By making data analytics accessible, companies give every business user the power to solve problems and make decisions quickly.   4. Unbiased AI Solutions Many AI experts have expressed concerns about the bias and limited perspective in building AI solutions. As AI-based solutions become a part of everyday lives, companies must be conscious about building unbiased AI solutions. Democratization of analytics will enable companies to gather various perspectives and develop a fair and ethical AI solution that serves different user bases.    5. Reduced Dependency on Data Teams Looking for an AI/ML solution that does risk profiling in the insurance industry or optimizes pricing strategies for customer retention? In the past, such requests were sent to the data team. But as the number of projects skyrockets, the data team is overwhelmed and unable to keep up with demand. This results in project delays, and other teams gradually lose their enthusiasm. Moreover, due to a lack of subject matter expertise, the data team finds it challenging to incorporate the crucial nuances necessary to enhance outcomes in the AI solution.    Analytics democratization can be a game-changer, for it empowers business users to reduce their reliance on data teams and build tailored solutions that address their specific requirements. Since most advanced analytics tools have drag-and-drop interfaces or do not require extensive coding, anybody