CNV3-I2-7- Ecosystem-Building Effective Analytics Ecosystems

INTRODUCTION

The digitalization process helps in accelerating the transformation and the creation of sustainable business. The generation of digital data offers tremendous opportunities for revising current business methods and practices. However, there is a critical need for appropriate data analytics ecosystems to suit the need of an enterprise. With the proper business analytics ecosystem in place a success in digital transformation leading to sustainable business can happen. However, an organisation should develop deeper insight exploit the potential benefits of data analytics.

Big data are generated from different type of sources, such as the multiple transactions performed daily, posts made on social media, or from the increasing number of sensors installed in numerous objects. Big data analytics is a tool that goes beyond pattern analysis, allows the prediction of events and supports artificial intelligence that is able to automatize processes, transform companies and create new types of business as it can do now, as well as to create value for the development of sustainable and prosperous businesses. For example, a company can gain a competitive advantage by investing in employee wellness programs that take advantage of big data. Specifically, companies may offer free wearable fitness devices to their employees if they agree to reach specific activity goals, urging them to exercise more and improve their health. The latter improves the quality of life of the employees and their families, increases productivity for the company, reduces medical costs for the government overtime, and may impact research as well since the data can also be used for medical studies.

There is a growing need for findable, accessible, interoperable and reusable infrastructures and data management standards that provide greater access to the information in the industry. Investing in such infrastructures enables innovation and digitalization of the business processes will start a wide-range of technology ecosystems. Digital infrastructures are now integral in numerous fields (e.g., business, health, transportation, finance), but the question remains on how we can give purpose to the data and extract actionable insight; by going beyond technical innovations and security issues, asking the right questions, and bridging business transformation with big data analytics for value creation that accelerates the sustainable development of the industry. Today, in analytics ecosystem none of the actors can be seen in isolation, instead all of them need to actively interact and collaborate with each other to create knowledge and innovate, while evolving their interrelations, leading to new technologies and companies, and increased value to the business. A comprehensive analysis of the big data and business analytics ecosystem and its interdependencies enables the development of frameworks that will provide solutions that benefit all the actors within the ecosystem.

The term ecosystem has been proposed to describe the interaction system, which includes living organisms and their non-living environment. An ecosystem in the area of management, technology and innovation is defined as managerially designed multilayer network that consists of actors that have different attributes, decision principles, and beliefs. Thus, such an ecosystem should be viewed as a highly complex system that can organize itself and requires long term data collection. Furthermore, as an ecosystem consists of multiple hierarchical layers, cooperation, collaboration, and coopetition among its actors is required but it may be difficult to be achieved. When referring to big data and business analytics, the term ecosystem describes the environment created and supported by the numerous actors, that comprise the ecosystem, their perpetual data generation along with their interactions and interrelations. Such ecosystems already exist in the industry within or between different sectors (e.g., Apple, Google, Intel, Microsoft). A strong analytics capability is key to digital transformation, as organizations that want to compete in the digital economy will have to invest in various resources including people, processes and technology of data and analytics To achieve this a data-driven culture is required, which will allow decision-makers to base their decisions more on insight rather than instinct. A big data and business analytics ecosystem comprise of the data actors (i.e., academia, industry/private organizations, government/public organizations, civil society, and individuals/entrepreneurs), who generate and use big data.

INGREDIENTS

A data analytics program requires a number of ingredients and requires requisite skill sets in analytics, business, and technical areas stating how big data analytics programs can be successful and take a deeper dive into analytics ecosystems. To create value through business analytics program there are many levels which need to be addressed.

Culture: The way the data is made available and used in the organisation
Governance: Security and governance for data in place in the organisation
Capabilities: Skill and capabilities required for data analytics
Financial support: Availability funds for putting analytical process in place/
Measurement: Metrics in place to measure the end results
Platform: Availability of right software with hardware support.
Value creation: Decision-making process followed by action
Confidence: Creation of trust across the stakeholders

The above level plays crucial roles in data analytics program towards strategy, architecture, training, data stewardship, and governance. They should link an organization’s objectives with the successful execution of its big data analytics program. Of course, there is no one-size-fits-all structure, process, or technology; it all depends on the analytics maturity, strategy, culture, and the needs of the organization.

BUILDING ECOSYSTEM

Invariable all functional areas and employees in the organisation are some or other way (directly or indirectly) are involved in data analytics program. Even if it is initiated in a small way as an experiment, but as it grows, it greatly impacts across the entire organization within and outside an organisation. A successful big data analytics program requires many interacting elements. It requires, of course, data, which has to be integrated from many sources, different types of analysis and skills to generate insights, and active stakeholders who need to collaborate effectively to act on insights generated. In the ecosystem there are other technologies used as tools, applications, and infrastructure and these are interdependencies and constant evolution, an effective approach to introducing, establishing, and nurturing big data analytics capability is to view its participants, components, and environment as an ecosystem, which is a network of interconnected and interdependent entities. A big data analytics ecosystem contains individuals and groups—business and technical teams with multiple skillsets, business partners and customers, internal and external data, tools, software, and infrastructure. Furthermore, an organization can be viewed within a larger data ecosystem that consists of other organizations and entities sharing and exchanging data to generate economic value.

A big data analytics ecosystem which is comprising of individuals and technologies, assemble the data that is required, analyse the data to generate insights, and determine actions based on these insights to achieve business outcomes. It covers technologies to introduce and sustain an analytics capability. The core ecosystem could be organized over multiple configurations as pe the requirements of organisation. It may be either centralized, decentralized or otherwise with the major criteria of visibility of the value of analytics. It should have ability to respond quickly to needs at all levels in the organization. This can be achieved through training, introduction of tools, innovation, and communication among analytics stakeholders. It requires some support roles such as administrator and project manager—would be necessary, and a single individual can perform multiple roles.

A key objective is to move organizations from discovery and experimentation with analytics to a systematic, pervasive application across different areas and groups with measurable business value. The CAO (Chief analytical Officer) leads the Analytics Ecosystem and coordinates resources to ensure analytics is used to deliver the desirable business outcomes.

Analytics architects leverage the heritage of business solution architecture that links strategy, business objectives, and constraints and develops a viable plan for execution. An analytics architect helps ensure insights are acted upon by feeding these insights back to enhance the business processes. This cycle of analysis, insight, and action has to be performed in a responsive, reliable, and scalable fashion. It requires not only understanding data and analytical models but also the operational systems, applications, business processes, and infrastructure.

In the data analytics ecosystem, the analytics ecosystem also contains the data repository—which may include a traditional enterprise data warehouse in addition to big data and Apache Hadoop-style data stores—along with discovery and analysis tools and the required infrastructure. Organizations are increasingly investing in analytics initiatives to steer day-to-day operations and improve business decision-making.

An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers for organizations to be more agile and adapt to changing demands rather than feel locked into legacy investments, talent pools, and processes. Instead of prioritizing the procurement of large-scale IT systems, CIOs can focus on forming and participating in these ecosystems.

Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own information-gathering and business processes, but those of their suppliers, vendors, and commercial partners. For example, health insurance providers that receive claims data might understand doctors’ utilization and treatment methods better than the hospitals themselves, so hospitals can collaborate with these companies to obtain useful information about their own operations and efficiency. An aircraft manufacturer can inform its suppliers whether they are running low on component inventory or whether components are meeting their contracts’ quality standards, which can influence these suppliers’ global supply chain and business practices. In such cases, organizations can deepen their own analytical capabilities and improve business processes while also boosting those of their ecosystem partners.

Existing organizational culture can hinder CIOs’ efforts to create and participate in analytics ecosystems. One way a company can overcome limitations is by setting up an external innovation lab to experiment with disruptive technologies such as machine learning, cognitive computing, blockchain and advanced analytics. This experimental - change management -strategy will help the to diffuse the cultural barriers to get the organisation adopt the analytical ecosystem.

CONCLUSIONS

Today, almost every industry has witnessed an exponential increase in volume and velocity of data which is being generated continuously, which is facilitated by tremendous increase of computation power. The competitive business environment has propelled most businesses to explore ways and means of generating insights and predictions from their internal and external data. Hence, every organisation needs analytics ecosystem to survive and deliver incremental business value, which is critical for the organisation’s success.

Dr Vinod Sople, Dean Research ITM Group of Institutions