Allan Engelhardt

There's a saying that I love from Sally Bigwood - "graphs are like jokes: if you have to explain them they have failed". Below is a great example of where the graph shows one thing - but with a more detailed explanation is found to display the complete opposite. #datavisualisationfail

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Rethinking data leadership. Many organisations invest in data tools but fail to generate real profit. It's time to stop treating data as a tech problem and recognise it as a profit driver. Explore how smarter data strategies can unlock true business impact: https://heyor.ca/JNxh2j #DataLeadership #BusinessImpact #DataROI #DataStrategy #Analytics #Innovation

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Certainly worth attending! Open source is a crucial first step in getting more value out of our work!💫 Imagine a world where rather than many of us using different tools to solve the same old problems and do the same old work, we solved problems once, together, and shared and continued to build on that solution. That's the goal of open source! 🚀 Please consider going along to the below and hearing from one of the rockstars of the health data science and analytics community! If you're still hungering for more you can read lots more about open source, why it's important and how to get started on our RAP web pages! https://lnkd.in/dQc3cFgr

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“Data leaders are faced with developing new and innovative ways of creating and implementing data strategies. The below suggests how an additional approach, based on principles that are recognisable across the company, can work alongside tried and tested frameworks in order to deliver against the data strategy challenge”

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Data literacy is the start, not the end. Data literacy has become the 'thing' recently, and for good reason. Companies have spent eye watering amounts of money building the data infrastructure and getting to grips with reporting, analytics. So now the question is, 'do the business know how to use the stuff we've given them'? Enter the data literacy programme. The future will go one step further. As hunger for disruptive technology increases exponentially, technical specialists will move closer to such disruptive tech as the market will naturally move this talent pool. Result - foundational expertise like Data Architecture will suffer an even bigger talent shortage problem than today. Solution - the end user will need to "self-architect" their data. They will need skills in running and maintaining 'the infrastructure', while tech/product teams drive the bottom line benefit for Boards and shareholders with the latest shiny toy. This in itself is going to require better Data Mgmt (incl. data architecture) tooling, but also better business-ready Data Management learning content targeted at the end-user, not the data professional. Lots to do. #dataliteracy #datamanagement #dataleadership

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https://lnkd.in/eJENWvBW Having departed from. However, 6 years on, it's great to see how far the DDaT programme has come along, with strong strategic outsets achieved and yet more set to be achieved. Distiniguished in its cloud-adoption solutions and Business Intelligence implementation (which, I was a part of a strong delviery-focused team, internally as well as external vendors). I will continue to keep a close eye over how successful this programme grows, into its strategic initiatives, and its adoption of contemporary technologies, to deliver value to the public, and wider audiences, and with the utilisation of driving a data focused organisation. #DDaT #Digital #digitaltransformations #datadriven

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Data Technologies - Do you need a Super car or an economy off roader? Data is a naturally an area that lends itself to technology, historically it was traditionally the domain of IT and therefore was somewhat removed from the business……This approach is less prevalent, but it does happen and that does relate to the perception that data is both technical and difficult; without business ownership and leadership, some data solutions can easily become the province of the technical teams rather than the business.It is very rare that a CRM or an ERP product selection takes place without the business team it does happen in the data space, partly because of the variety of data spaces that can exist…….it could be that you think you need data warehousing, integration, data governance, data management, analytics or reporting……all of these are data related but different and the technologies that support them can cross over, so you naturally lean into the more technical teams for understanding of these areas. There are come steps you can take to help navigate the shoals of a vendor rich environment: Do your research so you have an understanding of the issue that you want to address and the high-level approaches that exist. Find a trusted advisor; Establish their expertise in the areaof interest in but understand their relationship with specific technology vendors. Align your business case, the value proposition and the business strategy – you need to know whether your future technology needs to scale at speed or could be a ‘for now’ solution. Build a business Cohort – looking for a solution that only meets the needs of one stakeholder group is harder to get funding for than something that addresses challenges for multiple stakeholder groups, consult and include your technical team and recognise their IT ‘gatekeeper’ contribution. Gather your business requirements – identify what is in the ‘must have’ column and prioritse but don’t forget the ‘would like to haves’ – these are what will help drive future engagement, so be aware that you might need to add them to the solution later, this can impact pricing so think about the future roadmap. Be upfront about your budget – it is disheartening to take a ‘Ferrari’ demo when you can only afford an economy version. Don’t be distracted by 'shiny' offerings – it’s easy to get enthused over something really cool or clever that is not in your requirements but if you think you need it, you have to revisit your requirements and attribute business value to that ‘shiny’ offering….. Be pragmatic; the art of the possible is important but you probably don’t need to do it all this week or this year, you need to get your foundation in place first. Hopefully these steps, learned through my experience, will help you avoid ending up with an expensive Data ‘super car’, with lots of shiny bits, that can’t get you through the bumpy landscape that you need to navigate as you start your journey in the wilds of your data.

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It's a big birthday - Data Orchard's Data Maturity Assessment is 5 years old today! Nearly 12000 users from over 1000 organisations. Here's a celebration blog to mark the occasion #data #analysis #datamaturity #data4good #CEOs #nonprofits #charities #socents

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There is one big thing you probably don't realise about the diagram (which is excellent btw) from Dylan Anderson below. And that is that Timeliness and Completeness, probably two of the BIGGEST issues in Data Quality are in some ways not data quality issues at all. And that is because if there *is* no data (you could argue) there IS no data quality issue. Why is data not timely? Why does it matter? It happens because processes fail or data fails to get delivered on time. It matters because STAKEHOLDERS FIND OUT before you do --> and why is that? Because your architecture, and probably whatever is orchestrating things, isn't set-up well. The same is true of completeness. Apart from no-one realises. And then the system recovers. And then a stakeholders comes to you and says "hey where is last week's data by the way?" I think this really highlights the importance of ensuring you have robust architecture while also monitoring quality metrics both in a "blocking" sense (i.e. pipeline stops if something fails e.g. in dbt) vs. non-blocking (e.g. Data Quality testing tools or pipelines that are simply data quality tests). You can read some thoughts on this below And give Dylan a follow 😎 #dataengineering #dataquality #dbt

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𝗪𝗵𝘆 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻'𝘀 𝘀𝗵𝗼𝘂𝗹𝗱𝗻'𝘁 𝗯𝗲 '𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻'... I've had a lot of feedback relating to my earlier post on why organisations shouldn't be data-driven. My suggestion is instead to be 'data-informed', where data is a tool, not a dictator. I'm a strong advocate for using data - but not being enslaved by it, and instead treating data as you would other contributing information - as an important piece of the puzzle that requires consideration. #Data

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🧩 THOUGHTS WANTED 🧩 - What problems do Data Governance councils and domain forums solve? A client recently posed this question and I thought it would be great to ask my network for their perspective. Drop your thoughts in the comments below! #DataGovernance #TDGC

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From a 1973 book - but still so relevant to the aspiring data scientists of today.... My favourite diagram from my PhD thesis - which itself is a last century publication. The four pictures all have the same mean and same variance - which means if they are analysed using only second order statistics - they are indistinguishable. It amazes me that to this day so much data analysis inherently uses second order statistics - anything that has a least squares or Normal distribution foundation is in essence an analysis using second order statistics - the frills are just a fudge because this is now known to be wrong. Data science is an exciting field - but my take away from using data to solve problems for a number of years - working with some of the brightest people around - is that good data science is really hard and needs some amazingly unique skills. Good data science is about understanding and framing the problem well - then finding the mathematics that work for that situation. Much data science that I see is regurgitating techniques that were learned at university and are readily tractable mathematically - rather than for their suitability to the problem. The book is Richard O. Duda and Peter E. Hart, "Pattern Classification and Scene Analysis".

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Are you struggling to articulate a vision of what data can do in your organisation beyond enabling dashboards of 'dancing graphs'? If so I cant recommend this excellent blog from my colleague Stuart A. enough. Data is the life blood of complex organisations, however too often organisations are treating it as an afterthought, limiting their data vision to retrospective understanding and reporting. Stuart makes the case to refocus on building a data asset that proactively determines better outcomes across all aspects of your business, every minute of every day. #datafirst #azure #powerplatform

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We're building modelling tools in PowerBI. Exciting to be able to hand the NHS scenario tailoring tools to predict future workforce and capacity requirements. Attain #Analytics #Healthcare #PowerBi

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I really struggle with pithy data storytelling titles and would love advice on how to approach it with this chart. It's a silly, dirty and quick #30DayMapChallenge inspired by Ansgar Wolsing https://lnkd.in/eeMfiPHf I've filtered maps::world.cities for cities with a population of at least 1 million and drawn a circle from each city to its closest partner - that's what the purple area represents https://lnkd.in/e3V8cyqw

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Being a better analyst isn't about accumulating knowledge; it's about integrating it - seeing how it all fits together. At the risk of sounding like a fortune cookie, here is a practical example: Running some models on the Titanic dataset to predict likelihood of someone surviving is acquiring knowledge. Recognising that it can be used to predict the survival rate of customers in subscription service products using a bunch of different variables, and making recommendations to your stakeholders is integrating it.

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#QlikBots and I are multi-tasking this afternoon, #QlikConnect and #StudyTime. Mike Capone taking #opportunity and how 'Data is the Foundation', 72% of organisations note managing data is one of the top challenges to scalling AI. "Turn data into outcomes!" #QlikLuminary #DataAndAnalytics #Ai #DataQuality #DataTransformation #QlikBots

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Our new guidance is published! This guidance has been produced to drive an improved and consistent data scoring approach across the pensions industry, to achieve a comparable and understandable data scoring standard, and to ultimately drive improved data quality. If you’d like to discuss the guidance please feel free to reach out to me!

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