While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate. Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees. Discovering real business opportunities and achieving desired outcomes can be elusive. To overcome this, companies should pursue a constant attempt to re-engineer their analytics strategy to generate insight that leads to real outcomes
Re-engineering Infrastructure with Analytics
To successfully derive value from data immediately, there is a need for faster data analysis than is currently available using traditional data management technology. With the explosion of web analytics, social media, and the “Internet of things” (IoT) there is an opportunity to radically re-engineer data architecture to provide organizations with a tiered approach to data collection, with real-time and historical data analyses.
Infrastructure for Big Data and Analytics (BD&A) is the combination of components that enables architecture that delivers the right business outcomes. Developing this architecture involves computer science, which comprises aspects of design of the cluster computing power, networking, and innovations in software that enable advanced technology services and interconnectivity. Infrastructure is the foundation for optimal processing and storage of data and is an important part of Big Data and Analytics, which is also the foundation for any data farm.
The new era of analytics infrastructure is virtualized (analytics) environments also can be referred to as the next Big “V” of big data. The virtualization infrastructure approach has several advantages, such as scalability, ease of maintenance, elasticity, cost savings due better utilization of resources, and the abstraction of the external layer from the internal implementation (back-end) of a service or resource. Containers are the trending technology making headlines recently, which is an approach to virtualization and cloud-enabled data centers. Fortune 500 companies have begun to “containerize” their servers, data center and cloud applications with Docker (S. J. Vaughan-Nichols, ITWorld). Containerization excludes all of the problems of virtualization by eliminating hypervisor and its VMs. Each application is deployed in its own container, which runs on the “bare metal” of the server plus a single, shared instance of the operating system.
Business Process Re-Engineering using Analytics
The BPR methodologies of the past have significantly contributed to the development of today’s organizations. However, today’s business landscape has become increasingly complex and fast-paced. The regulatory environment is also constantly changing. Consumers have become more sophisticated and have easy access to information, on-the-go.
Staying competitive in the present business environment requires organizations to go beyond process efficiencies, incremental improvements and enhancing transactional flow. Now, organizations need to have a comprehensive understanding of its business model through an objective and realistic grasp of its business processes. This entails having organization-wide insights that show the interdependence of various internal functions while taking into consideration regulatory requirements and shifting consumer tastes.
Data is the basis on which fact-based analysis is performed to obtain objective insights of the organization. In order to obtain organization-wide insights, management needs to employ analytical capabilities on data that resides both inside and outside its organization. However, an organization’s analytical capabilities are primarily dependent on the type, amount and quality of data it possesses.
The integration of an organization’s three key dimensions of people, process and technology is also critical during process design. The people are the individuals responsible and accountable for the organization’s processes. The process is the chain of activities required to keep the organization running. The technology is the suite of tools that support, monitor and ensure consistency in the application of the process. The integration of all these, through the support of a clear governance structure, is critical in sustaining a fact-based driven organizational culture and the effective capture, movement and analysis of data. Designing processes would then be most effective if it is based on data-driven insights and when analytical capabilities are embedded into the re-engineered processes. Data-driven insights are essential in gaining a concrete understanding of the current business environment and utilizing these insights is critical in designing business processes that are flexible, agile and dynamic.
Re-engineering Digital Analytics – The new paradigm
It’s always of great interest to me to see new trends emerge in our space. One such trend gaining momentum with our enterprise customers solves an old problem with what I’d describe as re-engineering digital analytics. Brands are starting to augment and, in some cases, even replace their existing vendor-based web analytics implementations with their own in-house analytics solutions.
Just like everything else in our industry, changes in consumer behavior caused by mobile and social trends are disrupting the web analytics space. Just a few years ago, web analytics solutions gave brands the best view into performance of their digital business and user behaviors. Fast-forward to today, and this is often not the case. With the growth in volume and importance of new devices, digital channels and touch points, web analytics solutions are now just one of the many digital data silos that brands need to deal with and integrate into the full digital picture. While some vendors may now offer ways for their solutions to run in different channels and on a range of devices, these capabilities are often still a work in progress.
Many enterprises today find their web analytics solution is just another data source that must be downloaded daily into a multi-channel analytics data store and then run visualization tools like Tableau, Qlikview, or Domo to provide cross-channel business reporting internally. Assuming this is the case, an enterprise is really just paying the web analytics vendor to be an expensive data feed. This new reality is driving some customers to cut the cord on vendor-based web analytics solutions.
Re-shaping Analytics for Workforce Acquisition and Management
Analytics is causing a buzz in virtually every function but recruitment is one of the more recent to get a digital refresh. A new data driven approach to talent management is reshaping the way organizations find and hire staff, while the power of people analytics is also changing how HR tackles employee retention and engagement.
The implications for anyone hoping to land a job, and for businesses that have traditionally relied on personal relationships are extreme, but robots and algorithms will not yet completely replace human interaction.
Advanced analytics will certainly help to identify people in specific searches, but a known network of people you trust will likely remain the first place potential employers look.
Companies are increasingly using these state-of-the-art techniques to recruit and retain the great future managers and innovators, according to a report from McKinsey & Company, the consultancy.
Rather than relying on a rigorous interview process and resume, employers are able to “mine” through deep reserves of information, including from your online footprint.
The real value will be in identifying personality types, abilities, and other strengths to help create well-rounded teams. Also, companies are also using people analytics to understand the stress levels of their employees to ensure long-term productiveness and wellness.
The Final Word
Based on my experiences with clients, alignment among the three key dimensions of people, process and technology within a robust governance structure are critical to effectively utilize analytics and remain competitive in the current business environment. It is able to open doors to growth through market analysis resulting in the identification of industry white spaces. It enhances operational efficiency through process improvements based on relevant and fact-based data. It is able to enrich human capital through workforce analysis resulting in more effective human capital management. It is able to mitigate risks by identifying areas of regulatory and company policy non-compliance before actual damage is done. Analytics re-engineering approach unleashes the potential of an organization by putting the facts and the reality into the hands of the decision makers.