Digital enabled and integrated business models are proving to be essential to traditional businesses to thrive and be a growing, sustainable, going concern. According to this WEF article, Digital innovation can both generate value for business and unlock value for society.
Assuming your business has traditional IT systems for typical business processes, Data and Analytics is more likely to be the first of Digital technologies pit-stop to be able to unlock greater value from your existing business model.
Your business must be used to getting Business Intelligence (BI) reports which are in turn derived from data that are transferred from transactions systems. These BI reports help you assess what happened in the past. Your Business Managers, Strategists and Planners subsequently use these reports to prepare for the future. Using past performance as a tool to determine what could happen had been a good indicator of things. This has no longer been true with the growing integration into the global ecosystem, disruptive business models and newer competition offering a different experience all threatening the status quo. The current COVID-19 impact is a classic example. Nobody expected China to close an entire province. No one expected the aviation sector to close for business to the far east sector which has been helping their revenue growth. No one expected the shutdown to last for so long thus impacting global supply chain, impacting launch events and many more. This is where Analytics comes into play to start with before adopting Machine Learning and Artificial intelligence. The World Economic Forum has estimated that there are nearly 5 million businesses that are likely to be affected worldwide.
There are 4 types of analytics in the main. The basic one is the descriptive analytics which primarily helps you know what happened. And then, you have the diagnostic analytics, which helps you know why events occurred. You then move onto Predictive analytics to know if there is a pattern and figure what is likely to happen next. You then advance to prescriptive analytics where you can ask the analytics to evaluate the data and tell you what you could be doing next or explore what could happen if you were to explore the impact of a decision. The first two types help you know what happened in the past and the latter two help you assess the future potential.
There are many articles that have been published about reaping benefits from big data and analytics but this one from Forbes provides a holistic perspective in terms of associating the technology to business outcomes.
Most businesses would have descriptive analytics, even if it is spreadsheets, and many are likely to have diagnostic analytics. Savvy businesses, even traditional ones, who have invested in data and analytics integrated with other data sources are able to predict what could happen with Predictive Analytics. Some of them can ask “what if” questions to probe what could happen and make some good independent judgement. It is true though that any insights and foresights that are generated are only as good as the data that are utilised. For example, deriving an insight with just past performance data and supply chain closure will not help. You may want to probe what if scenario based on when the supply chain does get opened and furthermore, you may want to explore the impact of the stampede for prioritisation if it does occur. This does require investing in or utilising advanced Analytics and data software as well as data scientists. If you have already moved onto Cloud based SaaS, some of them would give you native machine learning based analytics which are useful if you are just about to start off.
What are the risks of missing out on the opportunity to your business? Here are a few examples.
Digital marketing and selling - Your business may have lost the opportunity of focused marketing that is search engine optimised, marketed at an inopportune time or lost the opportunity to engage buyers when they are ready to be influenced or ready to buy. Not all upsell and cross-sell work well all the time. Things are changing even for the Retail industry as the events of 2018 and 2019 revealed that not everyone has fallen for the Black Friday or Boxing Day or other event-based marketing and selling. For example, would it help your mortgage business to come up with optimal offers at a time when there are buyers needing it and, in the regions, that buyers would want? What can you do to offer different product mixes and offers using social media feedback? What if your business can be razor sharp?
Analytics led operational and services excellence – Your business could be having a business process improvement initiative. Such initiatives result in better execution of a process however, what if your business units can use data and analytics to discover product quality issues, spot problems and perform root cause analysis faster. What if your business can use service data to predict likely issues and fix them before they occur thereby, delivering a better customer service and ensuring customer retention? What if your business units can help your product users with predictive and focused training to prevent product break-down?
Data and Analytics led supply chain excellence - What if your business units are able to use data and analytics to predict likely product quality issues from suppliers, when they are likely to occur, their causes and prevent them so as to ensure you are having an optimal inventory and avoid productivity issues with reduced rejection? What if you can use analytics to align your buying with your actual sales cycle to optimise your key ratios?
Data and Analytics to detect fraud and optimise internal audits – What if your business can perform internal audits effectively and at lower costs using a combination of analytics and SaaS? What if your business units can use analytics to detect, predict and prevent frauds with known triggers and events to start with before charting into unknown territory?
Mergers, Acquisitions and Divestitures impact analysis – What if as part of your Due Diligence assessment, you can assess and predict the impact to your Cashflow, revenues, margins, assets and liabilities better. What if you can use data and analytics to compare the impact of scale deals vs. scope vs. capabilities augmentation deals better?
What are the risks to your business when adopting data and analytics?
Aligned decision making is crucial. You may want to maintain your revenue predictions by enhancing discounts. What if your operational production decisions are already made or if an issue like COVID-19 affect availability of supplies?
Neglected data and neglected analytics – This is the biggest risk and it is a carryover from statistics – as they say, there are statistics and there are statistics. You can get analytics tell you a great story from the data. But, is it just the data you have? Is it good enough to take a decision based on just the used data sets? What are the threats of not analysing the neglected data sets? Do your business units know the data sets that were neglected? Are there neglected data which, when juxtaposed with the basic data sets, and analysed, narrate a different story? As an example, we have more people employed and fewer unemployed in UK and yet, the consumer spend is low and GDP growth is not great. Is there a different data set that we are ignoring? As another example, how do we account for the climate change – the impact of either the increase in bushfires or the increase in flooding or the increase in droughts and water scarcity in certain regions? You may choose to go with the immediate analytics to start with and nurture your analytics further, i.e., start small and mature as you go along.
Trusted data sources – Can you trust the data? What if the data that is keyed in is limited? Are there mechanisms in place to verify the data? For example, it was not uncommon for pipeline analytics to go wrong since we were assuming that our understanding of client feedback was correct only to know that we obtained answers to the questions we asked.
Data protection – Are you protecting the data your captured, analysed and profited from? Are you also protecting the data that you procure, analyse and profit from?
Federated analytics and data traceability – Your business may not be able to perform all the analytics and there is a possibility that you are sharing the data with 3rd parties. Are you able to fulfil your data protection obligations effectively and prevent your organisation from reputation threats caused by data breaches?
Visibility of your data through API – How secure are your data lakes, libraries and catalogues whether they are visible internally or externally? How effective are your need-to-know basis sharing? Is visibility provided automatically or if approval is required, what are the effective check mechanisms? Who is responsible for how this data is used or misused?
De-bias the analytics – You can trust the data when it is from an original source. Are there effective mechanisms to remove the bias from derived data? Are there effective means to remove the bias from analytics? i.e., any analytics is based on how the algorithms have been written thereby potentially creating a perception of reality and may prevent you from perceiving through the reality. As example of bias, oil prices are predicted to go up and goes up even when there is a sneeze in the middle east however, the fact that consumption went down in the largest oil importing country following country-wide shutdown has not resulted in dramatic reduction. What is the possibility of manipulation of data and analytics here? Are the Analytics based on balanced algorithms?
Data exchange security – Are there effective means to secure the data exchange and are there validation mechanisms in place to periodically verify the sources and recipients of the data exchange?
Data management and Data governance – Is there a Chief Data Officer who is responsible for data protection? Are there designated owners within each Business Unit are entrusted with the responsibility to protect the organisation from data breach reputation threat? Data security is the responsibility of every individual in the organisation. Are there periodic trainings and affirmation systems in place for undertaking this responsibility?
Fact check mechanisms – It is great when the feedback is genuine on Social media. Performing social media analytics is great however, it is very easy for people who know how it works to easily manipulate it. Many examples of manipulation exist which does cause disillusionment to the ones who are wary. It is very easy to generate Likes on Social media by timing the broadcast of the post and communicating it to friends and family to influence it. Recruiter influencing through insertion of keywords to manipulate the AI algorithms. Avoiding the bias in assessment of photographs and postures – what is true for one country may not be true for another. Fact checking and removal of cognitive bias is extremely important to ensure independent judgement and taking the right decisions.
As with most things with Digital, Big Data & Analytics in particular, it is always recommended to start off with small pilots to achieve a Minimum Value Product and then move on in incremental steps to achieve a Minimum Loveable Product. This article, albeit old and focused on a specific sector, would help you to adopt a robust approach to reaping benefits from big data and analytics.
This is not meant to be an exhaustive list but more to provide pointers on the risks – opportunities and threats – and how to constructively challenge traditional enterprises moving in the direction of a digital enabled and integrated business when leveraging big data and analytics.
What are you dealing with as Digital risks in your Boardroom or effective ways to manage Risks in a Digital enabled and integrated business model? Look forward to engaging with you on topics that give your enterprise the edge.
#ai #chiefinformationofficer #itstrategy #chiefdigitalofficer #chiefdataofficer #chiefanalyticsofficer #dataprotection #datasecurity #datamanagement #datasharing #datadriven #datagovernance #datascience #machinelearning #datacatalog #datalakes #datalab #analytics #analyticsplatform #predictiveanalytics #prescriptiveanalytics #api #dataexchange #microservices #bigdata #data #dataquality #datascientist #datastrategy #cybersecurity #fraudprevention #fraudanalytics #forensics #forecasting #informationgovernance #cloudcomputing #saas #chairman #chiefexecutiveofficer #riskappetite #chiefriskofficer #chieffinancialofficer #nonexecutivedirector #boardadvisor #cloud #digitalmanufacturing #digitaltransformation #digitalstrategy #digitalsolutions #digitalmarketing #digitalhealthcare #customerexperience #customersuccess #smartcities #smartenergy #digitalgovernment #digitalinnovation #innovation #technology #leadership #azurecloud #awscloud #ibmcloud #oraclecloud #tableausoftware #qlikview #anaplan #salesforcelightning #watson #ibmwatson
Comments