Survive and Thrive in uncertain times Right now many businesses have frozen all but the most essential spend in order to stabilise their business and help customers as they deal with the resultant impact on cash flow of the current situation. But while these measures are the right approach short term, the issue with freezing spend and battening down the hatches is that when we get to the other side (and we surely will), businesses that have taken this approach and are slow to react will sadly get left behind. From Black Monday in 1987, to the bursting of the .com bubble in 2000 and then the financial crisis of 2008, history has taught us that businesses that mobilise and position themselves swiftly during or immediately after a crisis will be in a better place to mitigate risks, build resilience and capitalise on recovery led opportunities. Data and analytics for competitive advantage The value and transformative power of data and analytics should not be a secret anymore, especially in these times of uncertainty. “Data and analytics (is) more important than ever,” says Equinix CIO Milind Wagle. “You’re making decisions daily with changing information and reacting to factors outside your control.” In other words you need to be in a position to Understand your data, Act upon the Insights derived and then continually look to Optimise. Over the next few weeks, I will be outlining how you can recalibrate your business to navigate these uncertain times and then thrive as we recover from and adapt to the impact of the crisis. By the end of this series you will understand:
The key to ensuring your business is not left behind as the economy recovers is to have a clear understanding of your business, your operations, your customer behaviours and market dynamics. To do this quickly, you need to have swift and easy access to your data, be able to extract valuable Insight and then Act upon it. Businesses that do this will not only survive but they will begin to thrive as they ride the wave of recovery. The first part of this series will talk more specifically about the transformative power of data and the need for a strong analytics capability. If you’d like to find out more about how we can help you Recalibrate your Vehicle Leasing Business by helping you to mine your own data, please contact us here to request your free Discovery consultation and we’ll be in touch to schedule a call. Brendan JayagopalFounder & Managing Director. Blue Label Consulting
0 Comments
Ever wondered where to start when embarking on a new data science or analytics project? It’s hard to know where you should start once you have determined that you need to kick off a new project. Just thinking about all the tools and technologies you might need to employ can make your head spin. What do you do first, what data will you need to access and what skill sets will you need for successful project delivery? To help crystallise this, I’ve outlined the 10 key points which I believe are essential to the success of any data science or analytics project: 1. Understand the problem or opportunity you are solving for. Define the problem statement, align with business strategy and quantify the cost/benefit. 2. Consider the options you have. Understand what actions need to be taken and determine the impact of these actions on your P&L. 3. Begin with the end in mind and formulate the end state vision and road map. In other words, what does success look like and how will you get there? 4. Set the right expectations. It is imperative that senior management understand what data analytics can and cannot do and that they buy into this. 5. Check that the required data is available. Make sure it is accessible in the format and structure required to enable the analytics. 6. Understand the skill sets needed and ensure the project team is adequately resourced. Include business experts to incorporate business / industry knowledge. Where there are resource or capability gaps, get the help you need to plug these gaps ASAP. Do not wait until the project is on a critical path! 7. Put in place technology that provides the analytical capability required for current and future projects such as data manipulation, predictive modelling and data visualisation supported by a data infrastructure that enables fast, agile analytics. 8. Apply a project management system. Ensure that you have a methodical approach to make the process efficient and keep stakeholders informed. 9. Create an adoption plan. A successful analytics project ends with the business adopting the solution or applying the actions recommended by the analytics. 10. Build in a feedback loop mechanism to enable continuous learning and improvement. Where there are models involved, make sure there is ongoing model risk management and governance in place. Undertaking these steps means you have made sure that:
If you want to know more about how to deliver a successful data science or analytics project or indeed have a project that you need support with, we’d be delighted to offer you a free Discovery consultation. If you’re open to that, please click here to contact us and we’ll be in touch to schedule a call. Brendan JayagopalFounder and Managing Consultant Blue Label Consulting AND WHAT TO DO ABOUT IT! Most, if not all business leaders today, recognise the value of their data and analytics function without necessarily realising the benefits. As a result, many boards are looking at their Analytics function and wondering how and why they haven’t managed to extract real value. Outlined below are my thoughts on the top 10 mistakes businesses unwittingly make when trying to build their Analytics capability – and what to do about it. 1. Putting the cart before the horse Taking a tool or technology first approach rather than taking the time to understand what the business is trying to achieve could be a costly mistake. Start with the business objectives and only then determine the data, tools and technologies best suited to deliver those objectives. Remember, real transformation starts with Strategy not technology. 2. Ignoring the human element As you build your data infrastructure, it is all too easy to forget that it’s not just about the data. You already have experts within the business so why not use them? How you build in subject matter expertise as well as company and industry knowledge is key. It’s the combination of people, process and technology that delivers real value and actionable insights. 3. Trying to run before you can walk In other words, wanting to do advanced analytics and data science before putting the fundamentals in place. To avoid this problem, first determine what state your analytics capability is in, then where it needs to be in order to deliver today’s answers while still planning and building tomorrow’s capability. 4. No end state vision or clear data strategy road map Understanding the company’s long term business objectives is key to determining the role that your analytics team needs to play to help deliver them. The key question here is, what role should data engineering, data analytics, data science or machine learning take in delivering those objectives? 5. Limited understanding of the skill sets required It takes more than just good operators to create a First Class Analytics function. You also need business savvy analysts and data scientists as well as strong data and analytics leaders who understand the benefits and can influence at board level to help drive change across the organisation. 6. Unrealistic or misaligned expectations This can lead to falling for the hype peddled by ‘cowboy’ sales people and then blaming the tools or the analytics team when it goes wrong. Better to step back and understand where you are today, determine where you need to be and to have a clear, informed view of what the business needs from data and analytics. 7. Lack of senior management buy in It is imperative that senior management understand what data analytics can and cannot do. Unfortunately, it is often the case that senior management don’t really see the need for a strong Analytics team that adds real value. As such, they only partly buy into investing in the required skills and resources. When this happens, it will always be difficult to champion analytics across the organisation. Being able to demonstrate the potential added value in language that senior management understand is vitally important. 8. Handcuffing the Data and Analytics team This comes back to understanding what the data and analytics team can do and ensuring that they have the right data and technology resources to deliver. Don’t put them in a dark corner and restrict their access to decision makers or to data. Without data there is no data science, data analytics or data anything! 9. Thinking data and analytics is JUST a support function The Data and Analytics function, if deployed correctly, is a business critical function with immense transformative power. However, it is obvious that too many Boards are dismissive of the power that good data analytics can bring to the business and often invest just enough to achieve the bare minimum. Access to fast, accurate data and analysis enables more informed and quicker decisions which in turn accelerates business growth and profitability. Without this, the business will wither, become obsolete and out of touch with what customers want. 10. Giving in to personal bias People love to make decisions based on personal biases, intuition and the loudest opinion in the room – Don’t! While expertise clearly plays a role in decision making, strong analytics and data provide the best foundation for decision making. 11. Bonus point – Getting side tracked by BAU It is easy to tie up resource on today’s fixes rather than tomorrow’s solutions because they are always urgent! But as important as managing BAU is, growing the business also means you need to have the analytics team focused on the future. That being said, your team may not always have the bandwidth or skillsets to handle both. If so, perhaps this is the time to consider bringing in external expertise that can work strategically alongside your in-house team to deliver today while building for tomorrow. Summary Avoiding these mistakes is crucial in building a First Class Analytics function. In particular, the importance and value of senior management buy in and end user adoption should not be underestimated - but it often is! I make no apologies for repeating that real transformation starts with Strategy, not technology. You will see the benefits of this approach when the analysis outputs provide accurate and timely insight, which is then taken up and acted upon by the commercial teams to accelerate business success. The above is not an exhaustive list, and not all of this will apply to every business, but I’m sure that a number of these will resonate with you. If so, we'd be delighted to offer you a free 30 minute consultation to help determine where you are in your journey and how best to navigate the different stages to achieving analytics maturity. If you're open to that, please click here to contact us and we'll be in touch to schedule a call. Fleet Management and Data Analytics One of the most useful ways that data analytics can deliver operational benefit to a business is that of 'asset uptime'. In the fleet industry this means minimising ‘off-road’ time for the asset. It costs to have an asset out of action, both in lost revenue but also reputational risk. Most companies already keep maintenance history, but what triggers the management of that maintenance? Unfortunately, most of the time the trigger is the actual ‘down-time’ event – failure of the asset to perform when needed. However, while this provides information it is a reactionary method of managing assets and if used as the main operational policy, is a strategy that won’t allow proper planning and maximisation of assets. A better option A more effective method would be to plan and manage maintenance, based on more than just historical maintenance schedules or manufacturer guidelines. Instead, accessing and utilising your data on asset condition could dictate the required maintenance. In this way, the timing and nature of the work isn’t limited to general rules but instead becomes dependant on specific factors such as journey times (i.e. rush hour), telematics about journey routes (urban vs. extra-urban), nature of payloads, multi or single point drops (very important in the growing non-high street part of the economy i.e. retail delivery, internet shopping etc) plus other driver behaviours. But this isn’t always easily accessible. Solution delivery Planning is key to taking a proactive approach and Blue Label Consulting can fulfil that need by offering a joined up data analytics solution. Integrating telematics and vehicle data to co-ordinate service booking means the focus is on getting 100% of maintenance done via automated service booking lines. This method provides enhanced operational capabilities for the business including:
Working with Blue Label Consulting helps introduce more operational control within the business. This allows companies and fleet managers to let the day-to-day running of the fleet (and therefore business) run in a more streamlined and automated manner. This in turn creates more time to identify future trends in the market, better plan operational changes, reduce costs and increase profitability. If this is an issue for your business and you are interested in understanding more about this approach please contact us here. Brendan Jayagopal Founder and Managing Consultant Blue Label Consulting |
brendan jayagopalBrendan launched Blue Label Consulting in 2011. With innovative use of Data through emerging data sciences such as AI and other quantitative methods, he delivers robust analytics and actionable insights to solve business problems. Archives
February 2021
Categories
All
|