Data practice Setup
•Download as PPTX, PDF•
0 likes•36 views
These slides were prepared for the Data Meetup session conducted by ACS and Preacta on 26th of Feb2020.. They are addressing the real issues facing the industry with their data management programmes and provides details about the necessary activities that need to be in place to ensure an adequate Data Practice Environment. These slides will help genuine data practitioners to put the theory into practice despite the chosen framework
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Data practice Setup
Similar to Data practice Setup (20)
Recently uploaded
Recently uploaded (20)
Data practice Setup
- 1. Data Practice HOW TO SET UP …..
- 2. Document Outline % Success? Theory in Practice ROI Roadmap
- 3. Jan 2019: Gartner 80% of analytics insights will not deliver business outcomes through 2022 July 2019: VentureBeat AI 87% of data science projects never make it into production Nov. 2017: Gartner 60% of #bigdata projects fail to move past preliminary stages. Most of the good data initiatives fail
- 5. Resistance to Disruption? Focuses only on the benefits Politically Naïve No adoption from senior executives Lack of data literacy in all parts of the organisation Lack of digital leadership mindset
- 6. Resistance Behaviour Types If you want to change the way someone behaves, you must change the way they are measured. Virulent naysayers Passive resisters Reasonable challengers Organizational resisters
- 7. Document Outline % Success? Theory in Practice ROI Roadmap
- 11. Commitment Personality High Achiever Determined & Hard worker Self- Monitoring Innovative Persists through thick and thin
- 12. Enterprise Architecture Capabilities Information Architecture cultural shift from activity-based to process-based behaviour
- 13. Document Outline % Success? Theory in Practice ROI Roadmap
- 15. 3C’s Business Rule Compliance Cost CustomerInflated Low Regulation is an Opportunity what costs one dollar to prevent, costs ten dollars to fix and
- 16. Benefits of Data quality for the sake of improved end-to- end process performance can be a game-changer provided proper “motivation” is in place to drive a “cultural shift” Compliance Cost CustomerIncreasedIncome ImprovedProcess
- 17. Document Outline % Success? Theory in Practice ROI Roadmap
- 18. 7-Milestone Roadmap Step 1: Develop the Data Community Step 2: Define Data Governance Framework Model Step 3: Set Goals Step 4: Identify Common Data Elements Step 5: Build Enterprise data architecture capability Step 6: Develop Feedback Process Step 7: develop performance indicators using data value chain chain
Editor's Notes
- I tried to summarise Our keynotes for today into this simple outline using 4 main categories displayed on the screen By First asking, Do we have a problem with the Success Rates? Common pitfalls, How can we improve the situation by “Turning the theory into practice”, Recognise “Return on Investment from a data value chain”, Finally Summarise the discussion into a roadmap.
- Let me ask you all, how do you actually see where your organisations from a data driven capability? Your B I tools – who are using them and to what extent How much time it takes to produce a new report How consistent are your data science and data engineer teams in their work methodologies How many times do you repeat the same task for different IT projects, whether it is simple automation or as big as an ERP or CRM system implementation How many times you tried to work out where the data for this reports or integration is coming from and there’s no clue on what’s being done and why – how long does that take from your team When you’re trying to seek decision from senior managers how receptive and understanding of the data issue you’re addressing In your opinion do you do things tactically or strategically and do you know the difference I can keep going for so many more … How many times do you feel that you’re alone in this and everyone else around you is failing you Well, you are definitely not alone by these stats on the screen as they indicate that 80% of BI analytics do not deliver business value 87% of data science projects do not make it to production 60% of bigdata projects fail to move part preliminary stages.
- New ventage conducted their 2019 survey for Big Data and AI – that highlighted 69% of the surveyed organisations have failed to implement true data driven culture 71.7% forge their data culture 77% of business adoption to big data \ AI is still challenging But what did they identify as the reasons behind this ; 95% of the factors are attributable to cultural and organisation issue Lets see that list more closely – have you experienced any of them yourselves?
- “data revolutionaries” are focused mainly on ideal theory and just oblivious to how disruptive they are to most of the people They are naïve to the culture politics surrounding them – And while they are promoting the benefits such as “saved money”, “increased marketing” and “better decisions” they are surprised with a lot of resistance from almost everyone around them. Leader ship lacks the digital mindset as it is a skill that they need to learn and it is outside their comfort zone therefore afraid to be exposed Others are fearing to lose their jobs , hence low adoption rates Most business people don’t speak or comprehend data terminology and therefore they are very uncomfortable with your ideas and suggestions because they are out of their depth…. I have experienced a lot of resistance to the use of “Pivot tables” back in 2004 when I first used them to address some survey results – I found them to be fascinating to provide stats in multi-dimensions instantaneously with just a drag and a drop of the survey fields. I was told that it is a “very messy” report and hard to understand – the business preferred to see just a single value per question. I was trying to show them the power in seeing the value when 2 questions are answered in respective to one another?? This is what we now have realised as “Data Science” – yet 87% of them are still failing till now!!
- The yellow circle just describes the present sphere of your organisations today after all the disruption caused by adopting the data-driven behaviour. Every organisation is competing in this data analytics space – AI/ML etc… just out of the fear of missing out but not because they have identified a “real” business development goal that require this. That is why when they embark on the implementation – due to the lack of understanding and not being “data ready”, most of these initiative don’t show real outcome or change in the culture. Instead they become very complicated and people continue sticking to their comfort zone more than ever… address the “reconciliation” reports Think about your experience wherever you are and look at this summary that summarizes the resistance behaviour into the 4 categories Virulent naysayers – who always disagree to everything and encourage the status quo to resist Organisational resisters are the bodies and committee that approve budgets and project priorities and they are mostly prefer to follow the status quo and it is safer for them not to alter new waters. The virulent group impact them and they usually starve new ideas of resources, set barriers and beat who thinks differently. The reasonable challengers are actually the best resister group because usually they can see where things will not work and they are trying to advise on that – by listening to them we can create allies and support groups The passive resisters make up the majority of your people –who privately acknowledges the new data initiative and express their challenges with the current situations and issues, however, they have no sense of committing to you because it will not work for them. they are waiting to see which way the “political wind will blow” This group could be charged by individuals from the “reasonable challengers” who have tried to voice their opinions but were let down so they decided to become passive as they feel there’s no point to keep trying while others have the irrational power.
- Now that we have explored our working environments and its behaviour we shall move into How can we improve the situation by “Turning the theory into practice”,
- now that we have explored the major obstacles facing any data management programme – let’s look at how can this be resolved The secret is that we need an “Accountable Sponsorship Commitment” – I have used the above illustration to simplify the message and leave you with a picture to remember when you leave here. Simply I’m illustrating the data governance programme by building a home. Building a home requires a strong foundation on which the entire building will rest and the cement pillars which will erect the building and a roof that will hold it together and protects the entire building. The walls that makeup the interior and exterior design that allows the functionality of each part of the home but mainly connects it together in harmony Lack of sponsorship – funding and resource investment capacity is like no foundation to withstand the ownership Lack of ownership – means no pillars to withstand your building Lack of Commitment – is like having no roof to cover you and hold the walls together The protecting walls is the “Data Governance Framework”
- Sponsorship requires commitment to continue …. This is at the level of COO, CFO and CIO Business Development is a crucial sponsorship activity because it is the opening of new marketing to monetise the data asset. It is the creating of real demand what problem you can see, by which your data can resolve. Sponsorship also invest in resources (people) for their dedicated time and not after hours or when they have time, to work on their data. Also sponsoring the outstanding talents by promoting, their works and ideas, by the means of endorsing them – let them do it!!! We have addressed on the previous slides the major reasons behind the current failure rates – therefore to resolve for this issue there need to be a new ways by which staff are being measured, “Reward System”. Which will then support reducing their level of resistance towards more cooperative.
- These are the Pillars at the level of Directors, Business Heads and Domain Leaders Accountability rules and policies decisions Adoption plans Create objectives
- This characteristics is what makes everything works out Look for these characteristics in your people who you can then rely on their commitment – and for each one of us here in this room, work towards these characteristics to become a committed member of your community. At the centre of this equilateral triangle – there’s the “self-monitoring” character because a true committed person need always to self-check what am I doing, and for who, and why? A committed personality when they face the resistance we spoke about – as in the case of power structures barriers such as “lack of time” or “just do it as a one off” – they will rely on their innovative persona, to help them come up with a way of achieving it, as a small building block to be re-used in the bigger picture end-goal. And because of their determination and hard working person they will not worry about the time it will take them to think and do this building block even if they have to keep trying it few times. They know that this what will make them stand-out as genuine high achievers.
- This is a cultural shift to all existing business operations – because it is replacing the activity-based mentality with a process-based one! Hence the performance assessment need also to be replaced by a new “rewarding system” to encourage the culture change necessary. Now you can see how it is all coming together in building this “home” as you can see I have placed the “Information Architecture” at the centre as a red hot core. Information generated by the business activities create a demand for and IT solution but it is a stand-alone entity, that needs: Dedicated skilled data operations management team to operate on its “value chain” This core also creates the “clear boundaries” between Business and IT And while business architecture will advise what data is important to them and solution architecture provides the necessary tools, yet, both are dependant on that central core of information that will be generated, organised and consumed.
- Now let us understand our data value chain and what it consists by which we Recognise “Return on Investment”
- Three simple stages – Generate, Organise, Consume Between collecting data and monetizing it – it will inform How to improve the product How to reduce time and delays What other products are in demand according to customer preference How to reach out to selective customer groups Process quality control, compliance and risk management
- High cost is generated from Inefficient processes Errors Lack of customer satisfaction Non-compliance and risk penalties
- Increased Compliance reduces risk and the cost of penalties Increasing process efficiencies reduces the cost Increasing customer base generates income
- Finally Summarise the discussion into a roadmap.