Business Intelligence in the age of Analytics and Artificial Intelligence
ADVANCE TO INTELLIGENT DECISIONMAKING
LET US START WITH GETTING THE BASICS OUT OF THE WAY
– DATA IS NOT THE NEW OIL, IMPORTANT, YES BUT BUSINESS IS ABOUT DOING BASICS RIGHT,
– ALL BUSINESSES ARE NOT INFORMATION BUSINESSES,
– ARTIFICIAL INTELLIGENCE IS NOT INTELLIGENCE, JUST BEST GUESS
– DATA BY ITSELF AND DEVOID OF THE CONTEXT IS WORTHLESS
These days, one cannot pick any trade publication and not notice a convergence on dia del sabor, namely Analytics and its ‘on-steroids version’ Artificial Intelligence (AI). The collective wisdom points to the efficacy of data utilization to transform the business. Many of our clients have gone through this stage of data envy. Some continue to allocate disproportionate scarce resource of time and treasure to “monetize their data”.
Like all gold rush stories, this one is fraught with peril. The primary reason appears to be the significance attached to the technology as opposed to the business objectives. We’ve seen this before with corporations jumping from one tool to another in hopes that the next tool will be the one to save the initiative, company or the project. The problem is in training, or lack thereof, of the training in the organizations to ask the right question. In this, the question becomes the answer.
The goal, it appears, is to replace or at least augment an organizations intuition and experience-based decision making with an element of data centricity to avoid missteps and to shine a spotlight on certain blind spots. The data centricity assumes a level of Data availability and education or without this any further progress is impossible. We propose a following step approach to help clients adequately optimize the operations and leverage data assets where possible and necessary.
Automate and Optimize – most businesses have implemented some level of automation, some more than others, the starting point is that. Before one begins any large-scale transformation, challenge what is already implemented. Even if it is nothing more than a general ledger at this time. This is a foundational step to transforming a company. The key watchword in this stage is flexible and adaptable. Know that what we build here will need to be changed and rebuilt. The automation platform should be flexible and adaptable.
Digital from the outset – This is an appropriate analytical step in the organization’s life cycle, as the model should be to think innovatively about key aspects of the business. The approach here is to develop transformative competencies that have a potential to be disruptive, think Uber to Taxis, Airbnb to hotels and also operations transparency. In this stage the company starts to build data competencies.
Value creation – with the core of data collection in place, the organization may launch the build stage of analytics framework. The organization decides to make key data available to executives for informing tactical decisions and strategic choices. The key success factors in this process are:
Analytics Evangelist – an organization needs to allocate a role of developing data insights and educating line managers on the possibilities of data analysis
Speed to deliver – Time kills energy, this is true in physics as it is in companies. The organization should have the necessary strategy in place to make the results of analytics available to the managers within on a few days of demand
Keep it simple – an organization must simplify the learning and deployment of analytics by reducing rework. Its often helpful to choose technology that offer a consolidated BI and analytics approach.
Adopt and adapt – Reduce human variability – The weakness of the data driven decision-making is often in ignoring the human factors in adoption:
Education- Implement programs to drive overall data competency in an organization
Build Momentum through ease of access, make it easier for executives to get analytical answers
Implement and measure the use of analytical elements in the corporate choice selection
Reward the utilization of analytical tools
New applications – Encourage the use of analysis, by promoting new applications and new analysis. Share and promote.
Critique – Set periodic review points to analyze the decisions made and supported by hard measured data as well as decisions made without the adequate data. Compare outcomes.
Educate and Evolve – Business Intelligence and Analytics is a high ROI and can be implemented very cost effectively. The organization needs to ensure a steady process of training the users about new data and analysis being made available and effective strategies for utilization
In the end, Data can be a very effective tool available to the decisionmakers in an organization, the power of data becomes available to the organization as more investments are made in development of human capital to ask the right questions of the business. Features in modern BI and Analytical Tools can help users overcome delays and difficulties by automating aspects of data exploration and analytics development and delivering information answers and recommendations to users in the context in which they need it.
Jonathan Bach is a Client Solutions Partner at Visvero, Inc. He works closely with various F2000 clients in optimizing the use of professional services for “ADVANCE”; ing along the BI/ Analytics path. Jon is based out of the Visvero, Pittsburgh.
Arvind Handuu is a Practice Manager for Business Intelligence & Analytics at Visvero. Arvind is an analytics value purist. He believes that a BI & Analytics platform should be a self-contained and sovereign solution. “Its value drops to zero the instant you are using a different data source to inform your decision.”; Arvind is based out of Visvero, Pittsburgh.