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Data, Leadership, Innovation & Strategy Pt.1

August 22, 2019

Data, Leadership, Innovation & Strategy Pt.1ISB Global Data Leadership, Innovation & Strategy

Data, Leadership, Innovation & Strategy

The following article brings together thoughts, sources and information on how to gather data from operations and utilise it to develop strategy and innovation. There is of course no one answer to this opportunity, however, understanding, planning, creativity and open mindedness will help. Enjoy the read. Cherish the journey.

Data or rather ‘Big Data’ as it is collectively now known, is data gathered from a myriad of sources in operations, archived, stored and retrieved to supply information and intelligence to advance decision making. In the last few years data from the Internet of Things (IoT), connected devices and equipment has proliferated, allowing greater insight into field, shop floor operations and any extended mobile and web interaction. Increasing use of Artificial Intelligence (AI) and Analytics for automated learning, recognition, input and further scrutiny of output is now standard in deciphering large amounts of information.

Designing, collecting, archiving, retrieving and using this data has become an advanced science. Interpreting, analysing, recognising and leading teams to understand the patterns and opportunities that this data represents has become a specialist-engineered extended business unit. This unit is very different from business as usual and a traditional department. It requires significant change enablement and is woven into the fabric of the organisational process. Gathering ideas and opportunities to advance company, revenue and competitive edge. Thankfully tools for collection, storage, retrieval and analysis are commonplace, however, innovation team management and go to market process is not as easy.

Principles of innovation are founded in the desire to achieve growth, leadership, performance and value. Innovation has become the lifeblood of any organisation and just as productivity and mechanisation was discovered and refined in the industrial revolution, so technology allows us to understand and advance productivity, service, value, profit and of course survival. People, culture and aspirations take the understanding and interpret into new optimised ideas, solving problems and as technology has provided the input, so technology is utilised to drive the solution, output and result.

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Data Management – Technical & Organisational Approaches to Create Greater Value

The value of data is truly realised when the operation can collect and utilise data to make better decisions. This is difficult to complete the larger the organisation and the more disparate the software applications used. However, there are now cost-effective software solutions to help with these tasks. Bi-Modal platforms allow for the integration of legacy, applications and devices, aggregating in quick to perform workflows that can capture, store and retrieve new data sources to be used in specialised predictive and pervasive analytics applications.

Traditional data capture and use in reporting has been utilised to increase productivity. Now, with the ability to now build or rebuild new systems quickly to capture additional data. That includes extended systems in the field, Internet of Things (IoT), mobile or web interactions, yield far greater results that allow correlation and greater simulated types of analysis. Key is both how to collect this data as part of an automated process and now how to store it, retrieve it and utilise it for multiple applications. Consider different departments requirements for data in the end to end process and how they use it. This 360 degree view will start to aid in collection, storage, retrieval and of course use for greater insight and intelligence.

There also could be a range of technical barriers to consider, especially prevalent with older data stored in legacy applications. Formats, encryption and data captured with business logic may only make sense if used in parallel with that logic. If certain business functions gathered specific data how does this affect formats, use, models and repositories? When designing modern systems as part of digital transformation programs, designing new capabilities for capture, storage, retrieval and consuming data from the outset is of paramount importance.

Data and its consumption are the cornerstone of a digital transformation program. Information work and intelligence output are an increasing part of a worker’s productivity and performance quota. So, supplying analytics platforms with an underlying data structure to support is key if architecting data and insight at the very core of the organisation, especially when building in experience for stakeholders.

Data is a fuel in which to power every aspect of the operation. It is the component to actions, events and steps throughout the entire process. It is the underlying principle of digital transformation. Every company wants to move towards a more agile existence, which is what people think of when mentioning digital transformation, but a metadata platform is required to organise the data for collection, further, greater utilisation and continual improvement.

Data is a strategic asset, create this culture as part of the digital transformation and everyone needs to take responsibility in collecting, governance, quality, utilisation and the ongoing value for the company. The only scalable solution, bearing in mind the quantity of data companies are now gathering, is to collect and clean on a repetitive basis preferably with controls to automate and check while acquiring, rather than large amounts of cleansing after collection.

Artificial Intelligence is playing a bigger part in automating the tasks associated with collecting, cleaning, quality and identifying sensitive data and ensuring protection. There is so much data to manage, it is now beyond human capacity to administer this amount of data. New tasks for humans are to interject and think creatively when making judgement calls in these data oriented activities, which currently is beyond many commercially applied and available AI functions.

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Data Culture – Changing Hearts & Minds to Focus Leadership & Drive Growth

It’s no surprise that data and analytics is quite overwhelming. Data and analytics are reality of modern life and organisations have to motivate, organise and approach with vigour and a healthy culture. There are constant risks, huge benefits, skepticism, tools and environments to manage. Data and the required culture should support business activities with talent, tools and decisions making, resulting in greater insight, stakeholder engagement and clearer, sharpened objectives.

Data architecture, analytics and the understanding should be led by business objectives. When these processes are in place results can be returned to the stakeholder that wants it, whether ideas, insight or innovation or helping the customer on an experience. The real culture change is using data on a regular basis to understand, set benchmarks and continually improve. Then using analytics to identify and iteratively increase that understanding and what more can be done. Start simply and work your way into improvement in small steps, if you are analysing your data properly, organic questions will start to form that will lead to improvements, then scale. Create your new data culture around striving for better decisions. It really is enlightening.

The business objectives should focus at least in part on innovations. Ideas for growth, cost savings or creating greater value in some way. Try to iteratively deliver on these objectives (discussed below in the innovation section) and utilise the data to inform and to back up the decisions. Within the team keep ideas and information flowing within the culture. If everyone can see everyone else’s ideas and how they got there, the good ideas will get to the top, the not so feasible ideas will dissipate. When everyone has access to the analytics platform, they can start to work with data to extract accurate, trustworthy information, behaviours will radically change. The team becomes interested in the intelligence ready to be unlocked in the process and systems holding the data, knowledge drives greater insight. Known as democratisation of data.

Managing Risk

Data culture focuses on risk. As an organisation in an industry sector there will be associated risks within operations. Take a recycling plant for example. There is a risk of fire, environmental impact and employee injury. If interaction with systems occurs as part of a digital process, shift logon for example a range of instructions can be delivered as part of a combination of training, instruction and getting data on how advanced an employee is within a skilled area.

There’s using data in process to help with the advance of operations activities like EHS. Of course, there is also the focus of ethical, social and regulatory activities with the use of data, how it is acquired, consumed, altered and stored. The process and use case should be accurate to detail what can and cannot be completed with the data that is being processed. This is especially prevalent when dealing with personal data or correlating data based on an individual, deemed private and collected, analysed and stored with behavioural data. Understanding resiliency and opt in for this type of analytics program is critical.

Data needs to be managed according to legislation, policies, procedures and rules. Once in place and all employees have been onboarded to principles, process and importantly rule orientation. Innovation programs can start to investigate data use, data science and data analytics for the innovation program objective. The key is to integrate both programs, this is a similar structure and methodology as bi-modal operations. Mode 1 is the robust, statutory framework for use, mode 2 is the innovation and actual use for growth and productivity.

Building teams to implement change calls for a multidisciplinary approach. This requires a collaboration of data science and frontline operations. Bear in mind, not everyone in the team is going to be conversant with data and analytics principles.

It is incredibly beneficial to have a cross section of the business that make up the team. If this represents the entire process, then you can call on expertise from a process area to build a use case and work on the innovation program from that point. It’s a balance of careful understanding, clarification and definition to find that solution that can be progressed. It also requires a connection and liaison between all the actors in the team. The mix, engagement, communication and interaction to drive the solution is of paramount importance.

You can only go so far internally, and there has long been the impetus to involve external stakeholders in activities to increase value of innovation through the interpretation of data. Through incentivising a collaboration partner with the vision and how to get there, a company can build greater advantage and service from the actor’s involvement in the transaction and data capture in the process. An organisation should be mindful of working with a suitably motivated partner. Consider the control of the data used and any legislative impact the use of this data may have on the innovation program.

Visit Data, Leadership, Innovation & Strategy Pt.2 to find out more on setting up teams

Thanks to McKinsey Insights for a constant source of inspiration.

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