Data, Leadership, Innovation & Strategy Pt.1

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

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.

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.

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.

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.

Data, Leadership, Innovation & Strategy Pt.2

Capturing Innovation, Creating Value, Generating Concepts from Stakeholders

Innovation is the most important way to drive profitable change in a business environment. Traditional

product and service categories are being superseded to make way for pioneering innovations in process,

distribution, value chains, business models and the very function of management.

Stimulating innovation is very difficult, sustaining it at scale so it is valuable is even harder. Motivated

teams are the bedrock of innovation programs and therefore need to be embedded into an organisations

culture. Benchmarks for encouragement and management need to be tracked and measured as

performance indicators against growth. The creation of dynamic, existing team talent without massive

resource investment and allowing the culture of innovation within team boundaries needs to thrive and

prosper. Furthering development of the innovation environment to cultivate trust, collective risk and the

expression of ideas, which when combined can be more motivational than monetary incentives.

Innovation Leadership

It is crucial that innovation, like any other management culture to be ingrained, is led from the top down.

Innovative behaviour is driven from a culture, to lead that culture from the front is imperative if intending

to change the culture to one of innovation. Innovation should become part of the core strategic planning

process including budget and forecasting. Strong leadership is considered to run in parallel with strong

innovation culture. The way leadership behaves sends strong signals to employees. Short term

performance goals can oppose those of longer-term change in moving a culture toward innovation.

Leadership teams and individuals need to be accountable, driven by targets and metrics according to the

type of objectives and program. Innovation needs to be defined and directed according to the objective

e.g. customer experience. Targeted with metrics to create value, scale and to meet objectives e.g.

increase product sales by X% over Y time frame. This must be committed to and habitual to develop the

culture. Setting traditional core targets, a new ambition should be set to change behaviour, for example

number of news ideas with go to market plan in the last 12 months. This could also be extended to

involve certain employees or external stakeholders. Focus should be balanced between what type of

innovation, teams and value. Equally on the targets, metrics and budgets, as would any budgeting and

forecasting business strategy disciplined exercise.

The Architecture of the Innovation Cycle

There are always going to be employees in the organisation that are ardent change agents and lean

towards innovation. Others, less so. Recent studies have shown that academia and creativity matter less

when compared with the ability to design connected networks in order to share innovations and adopt

quickly. Ideas generate more ideas and so need to be utilised to gain knowledge from different people to

feedback on ideas. The more valuable input the more valuable the idea.

Analysis on the network and cycle can deliver information on cooperation and those who go about

disseminating and nurturing ideas and information. This can also be very helpful when cultural shifts need

to occur, the case for this is often. Making networks decentralised to improve collaboration and

performance with larger numbers of connections helps active participation. As does different skills and

attitudes comprised of idea generation, asking the right questions and taking risks on experiments. Mining

data uncovers patterns which can serve as new ideas and insights providing input. Experts drive

proficiency and end results and producers can achieve while connecting to the business and maintaining

the network.

Connect Network

Find the right people to administer the network. Combine teams of employees that compliment and

combine to make a good team. Mix seniority, skills and performance. Name the network and any

subnetworks, assign tasks and objectives.

Define Roles

Designate tasks and communication protocols. Broadcast strategic objectives. Establish targets and goals

for success. Complete time relevancy. Foster Trust.

Back & Encourage

Determine leadership, technology and administration required to support team. Define knowledge and

information inputs from internal and external teams.

Manage & Monitor

Determine recognition from contributions. Formulate key performance criteria for individuals and teams.

Decide on progress and success tracking measurements. Responsibilities and task for assessment and

review. Process of impact. Member management.

Abstract Thinking, Pragmatic Operation

There must be a careful balance between leader and professionals to foster the development of the

innovation culture. Attitude, value and behaviour deliver openness and new ideas. Trust and engagement

result in strong performance. Leaders need to be mindful of supporting the professionals in place to

innovate, while professionals need to work hard at developing and continuing with a positive culture for

innovation.

Innovation inhibitors include bureaucracy, hierarchy and fear of failure. And promote short term

performance rather than encouragement of longer-term initiatives. Implement new ways of coaching,

stakeholder and departmental collaboration and learning from failure. Which is a change program in

itself!

Reflecting on own behaviours, role modelling and organisation mechanisms committed to with

motivation to build capabilities will help promote innovation. Promotion is a core element to the practice

of change management to create an effective innovation program. Key personnel should be transformed

into innovation leaders to ensure the network is productive. Manage experiments and quick success to

deliver rapid innovation change. Do not focus so much on the life changing innovative end result but in

the program itself - deliver speed and involvement of others. Positive interaction by team members will

help propel the program and result in experience, ability and effectiveness.

Insight into Innovation Performance

The team can measure and benchmark against a huge number of prospective metrics and performance

indicators when designing the innovation program. It is a good idea to set these metrics up and review as

you go but be mindful of setting too many. Capturing as part of the process to output automatically and

analyse how and what your achieving, especially if the process and teams have a large amount of size and

complexity about them.

Consider the output of the innovations program too, not just the activities that are involved in the

program in order to get to the output. Examine what is the output, what is the innovation for? Is the

investment in the innovation productive?

So, again, principles of innovation are founded in the desire to achieve growth, leadership, performance

and value. Technology allows us to understand and advance productivity, service, value, profit and of

course, survival. Therefore, alongside the innovation program metrics there should be typical and

traditional indicators that measure forecast and actual growth and profit. The scale of how valuable the

innovations program output is will be realised by real economic impact - profitable sales over time.

These are core Return on Investment (RoI) principles for the innovation program. The difficulty is what

activities to include in the program. Fundamentally this is the investment resulting in measurement in

sales over the distance (if no growth and scalability, why invest?) It could be worth looking to external

reports, peer company data, cycle times and benchmarks to find this information. Then setting achievable

objectives from these metrics within the comparable industry sector. Note, depending on the size and

complexity of the innovation program and cycle, will justify further requirements for analytics to

understand and uncover performance.

Another difficulty could be the benchmark performance of the new product against comparable product

or service? If the product and service is completely new and innovative, what can it be measured against?

So, if the innovation is a cumulative move in development, worth tracking the metric before the

improvement was made. It would also be a good idea to look at how the forecasted growth in the

improvement will impact the investment in the first place. So, there is quite a lot to think about!

Where possible, continue to use tried and trusted software platforms to deliver predictive and pervasive

analytics to test performance forecasts. Also, bear in mind that adoption cycles and time to market may

have an impact on profitability margins, if product or service has direct revenue attributed. However,

creating innovation from products, services, an experience or model may not directly have a revenue

stream assigned and could be advancing improvements or disruptions to create greater value, whatever

that value might be! Good luck.

Data, Leadership, Innovation & Strategy Pt.3

Innovation Checklist

Don’t just optimise, although it’s a good start, innovate!

• Facilitate motivated and organised individuals and teams to achieve rapid and repeatable innovation

cycles

• Explore business, market and technologies that can transform into winning value propositions

• Enhance your innovation programs by involving and integrating external networks and actors

• Devote resources, time and effort to a balanced set of activities with a measured income

• Innovation is a complex process, develop practices and patterns to help support and stimulate

• Pursue innovation relentlessly through aligned targets in the program

• Advance new models that can turn into a stronghold platform for continued innovations

• Deliver momentum and motivation to initiate innovation and process rapidly to beat competition

• Graduate innovation proportionately according to risk, market size, scope and predicted value

proposition

Pursue

• Deliver innovation at the right price for focused growth objectives, obsess on aspirations and value

• Create responsibility and accountability and quantify innovation targets directed on growth

• Include the quantified targets in business plans to secure innovation investments

• Allocate targets to teams and SBUs, reporting results in defined cycles

• Support feedback loops on targets and ensure everyone and everything is transparent

Embrace

• Focus and support the innovation ideas that facilitate best growth and scale

• Invest sufficiently setting goals and boundaries to manage risk rather than eliminating it

• Fund innovations with prospective pipeline budget, capture and work with promising ideas, stop

ideas that do not meet strict growth and scale objectives

• Dedicate time to ensure the correct assessment of process development, composition, value,

timings, duration and value

• Strategic initiatives should be adjustable and balanced short-term ideas, with risk parameters,

gauging potential for success and resource dependent

Determine

• Genius and creativity in teams will always be in short supply, nurture, invigorate, promote and

reward

• Methodically and systematically analyse the three innovation areas value able problems to solve,

technology to enable that solution, the business model that generates the revenue

• Organisations that collect data to support synthesis and amalgamation of these areas stand the

greatest chance of success

• External partner and stakeholder involvement will support insight and provide greater chance of

success

• This process is iterative, and prototyping, developing, testing, validation and refining will ensure

greater success

Advance

• Focusing on business model innovations that modify economics of value chains, profit streams and

delivery models are a increasingly strong area to innovate in

• Balance between new products and service but do not leave core models until under threat and hope

that it is not too late

• Promote monitoring, access and analysis to market intelligence data sources to identify opportunities

that are external to the core structure

• Evaluate the value chain constantly to provide focus on models that could deliver new customers

• Advocate pilot projects and experiments to reinvent thinking and perception, stress test value

propositions and prospective innovation initiatives against competitor countermoves

Initiative

• Fast track to go to market stage by developing the innovative ability to elude traditional decision

making process

• Accelerate to market should not bypass the cross functional collaboration required and the

meticulous learning cycles in place that enable innovation

• Innovation process should allow for streamlining through company protocols to create, deliver and

maintain competitive advantage without exposure to unnecessary risk

• Maintain the initial focus on the innovation and test early on focus groups to ensure the value

proposition does not become diffused

• Specification, budget, time to market, dedication, ethos and responsibility from leadership and team

is of paramount importance and should always be measured and tracked

• Marketing should champion the interest of users, providing a feedback loop into the leadership team

to ensure the result is what was envisioned, quick feedback results in quick change and quick

redelivery as necessary

Escalate

• Understand the magnitude and reach of the idea to ensure the correct resource and risk are

apportioned to the project

• Resource and capability must be mobilised to ensure innovation program can be delivered at volume

and quality. Facilities, suppliers, and distributors throughout the value chain must be prepared to

execute and roll out

• Market penetration and rapid scaling is vital to success is as important as the innovation program

and the technology it utilises to solve the problem

Expand

• Innovation requires collaboration from external stakeholders. Talent and knowledge transcend

company and geographical boundaries. Extending investment into ecosystems to harness and

capture valuable input is a must

• Share costs with different and faster solutions to go to market. High performing innovation teams

invest heavily in systematic partnerships and people, striving to become the choice of

partnership. To excel in this way ensures the best ideas will be in your realm

• Companies that practice the use of external networks ensure that the ecosystem is optimised for

their innovation process benefit. Funneling activities and specifics to further the innovation

process and approach of go to market

Activate

• Develop an aspirational culture that forges connection between innovation, strategy and

performance

• Setting financial targets for innovation focuses teams and individuals

• The Innovation process is generated by iterative projects and developing responsibilities using the

appropriate incentives and rewards

• Organisational changes may be necessary to promote collaboration, learning and

experimentation, sharing information and ideas freely, reviewing and optimising the structure of

project teams, ensuring that even if unsuccessful innovation efforts are recognized and rewarded.

Value failure above all else

• If in a larger company the set-up time can take a long time to establish, so focus on small

dexterous groups which are not constrained by normal working environments and cultures

building new ways of working that can be assimilated into the larger organisation.

Thanks to McKinsey Insights for a constant source of inspiration.

https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/strategy-

beyond-the-hockey-stick