Tag Archives: data literacy

The Art and Necessity of Building a Data Culture

By Ben Mann, Policy & engineering nerd. Technology & data evangelist. Working for @DAIGlobal. The original appears here.

We live in the digital era. And the digital era is built on data. Everyone in your business, organization, agency, family, and friend group needs data. We don’t always realize it. Some won’t acknowledge it. But everyone needs and uses data every day to make decisions. One of my colleagues constantly reminds me that we are all data junkies who need that fix to “get sh*t done.”

 

So we all agree that we need data, right? Right.

Now comes the hard part: how do we actually use data? And not just to inform what we should buy on Amazon or who we should follow on Twitter, but how do we do the impossible (and over-used-buzzword of the century) to “make data-driven-decisions?” As I often hear from frustrated friends at conferences or over coffee, there is a collectively identified need for improving data literacy and, at the same time, collective angst over actually improving the who/what/where/when/why/how of data at our companies or organizations.

The short answer: We need to build our own data culture.

It needs to be inclusive and participatory for all levels of data users. It needs to leverage appropriate technology that is paired with responsible processes. It needs champions and data evangelists. It needs to be deep and wide and complex and welcoming where there are no stupid questions.

The long answer: We need to build our own data cultures. And it’s going to be hard. And expensive. And it’s an unreachable destination.

I was blessed to hear Shash Hegde (Microsoft Data Guru extraordinare) talk about modern data strategies for organizations. He lays out 6 core elements of a data strategy that any team needs to address to build a culture that is data-friendly and data-engaged:

Vision: Does your organization know their current state of data? Is there a vision for how it can be used and put to work?

People: Maybe more important than anything else on this list, people matter. They are the core of your user group, the ones who will generate most of your data, manage the systems, and consumer the insights. Do you know their habits, needs, and desires?

Structure: Not to be confused with stars or snowflakes — we mean the structure of your organization. How business units are formed, who manages what, who controls what resources, and how the pieces fit together.

Process: As a systems thinkings person, I know that there is always a process in play. Even the abscence of process is a process in and of itself. Knowing the process and workflow of your data is critical to flow and use of your culture.

Rules: They govern us. They set boundaries and guiding rails, defining our workspaces and playing fields.

Tools+Tech: We almost always start here, but I’d argue it is the least important. With the cloud and modern data platforms, with a sprinkling of AI and ML, it is rarely the bottleneck anymore. It’s important, but should never be the priority.

Building data culture is a journey. It can be endless. You may never achieve it. And unlike the Merry Pranksters, we need a destination to drive towards in building data literacy, use, and acceptance. And if anyone tells you that they can do it cheap or free, please show them the exit ASAP.

Starting your data adventure

At MERLTech DC, we recently hosted a panel on organizational data literacy and our desperate need for more of it. Experts (smarter than me) weighed in on how the heck we get ourselves, our teams, and our companies onto the path to data literacy and a data loving culture.

Three tangible things we agreed on:

💪🏼Be the champion.

Because someone has to, why not you?

👩🏾‍💼Get a senior sponsor.

Unless you are the CEO, you need someone with executive level weight behind you. Trust us (& learn from our own failures).

🧗🏽‍♂️Keep marching on. And invite everyone to join you.

You will face obstacles. You’ll face failures. You may feel like you’re alone. But helping lead organizational change is a rewarding experience — especially with something as awesome as data. It’s a journey everyone should be on and I encourage you to bring along as many coworkers/coconspirators/collaborators as possible. Preferably everyone.

So don’t wait any longer. Start your adventure in your organization today!!

Improve Data Literacy at All Levels within Your Humanitarian Programme

This post is by Janna Rous at Humanitarian Data. The original was published here on April 29, 2018

Imagine this picture of data literacy at all levels of a programme:

You’ve got a “donor visit” to your programme. The country director and a project officer accompany the donor on a field trip, and they all visit a household within one of the project communities.  All sat around a cup of tea, they started a discussion about data.  In this discussion, the household members explained what data had been collected and why. The country director explained what had surprised him/her in the data.  And the donor discussed how they made a decision to fund the programme based on the data.  What if no one was surprised at the discussion, or how the data was used, because they’d ALL seen and understood the data process?

Data literacy can mean lots of different things depending on who you are.  It could mean knowing how to:

  • collect, analyze and use data;
  • make sense of data and use it for management
  • validate data, be critical of it,
  • tell good from bad data and knowing how credible it is;
  • ensure everyone is confident talking about data.

IS “IMPROVING DATA LITERACY FOR ALL LEVELS” A TOP PRIORITY FOR THE HUMANITARIAN SECTOR?

“YES” data literacy is a priority!  Poor data literacy is still a huge stumbling block for many people in the sector and needs to be improved at ALL levels – from community households to field workers to senior management to donors.  However, there are a few challenges in how this priority is worded.

IS “LITERACY” THE RIGHT WORD?

Suggesting someone is “illiterate” when it comes to data – that doesn’t sit well with most people.  Many aid workers – from senior HQ staff right down to beneficiaries of a humanitarian programme – are well-educated and successful. Not only are they literate, but most speak 2 or more languages!  So to insinuate “illiteracy” doesn’t feel right.

Illiteracy is insulting…

Many of these same people are not super-comfortable with “data”,  but to ask them if they “struggle” with data, or to suggest they “don’t understand” by claiming they are “data illiterate” is insulting (even if you think it’s true!).

Leadership is enticing…

The language you use is extremely important here.  Instead of “literacy”, should you be talking about “leadership”?  What if you framed it as:  Improving data leadership.  Could you harness the desirability of that skill – leadership – so that workshop and training titles played into people’s egos, instead of attacking their egos?

WHAT CAN YOU DO TO IMPROVE DATA LITERACY (LEADERSHIP) WITHIN YOUR OWN ORGANIZATION?

You might be directly involved with helping to improve data literacy within your own organization.  Here are a few ideas on how to improve general data literacy/leadership:

  • Training and courses around data literacy.

While courses that focus on data analysis using computer programming languages such as [R] or Python exist, it might be better to focus on skills-development on more popular software (such as Excel) which is more sustainable. Due to the high turnover of staff within your sector, complex data analysis cannot normally be sustained once an advanced analyst leaves the field.

  • Donor funding to promote data use and the use of technology.

While the sector should not only rely on donors for pushing the agenda of data literacy forward, money is powerful.  If NGOs and agencies are required to show data literacy in order to receive funding, this will drive a paradigm shift in becoming more data-driven as a sector.  There are still big questions on how to fund interoperable tech systems in the sector to maximize the value of that funding in collaboration between multiple agencies.  However, donors who can provide structures and settings for collaboration will be able to promote data literacy across the sector.

  • Capitalize on “trendy” knowledge – what do people want to know about because it makes them look intelligent?

In 2015/16, everyone wanted to know “how to collect digital data”.  A couple years later, most people had shifted – they wanted to know “how to analyze data” and “make a dashboard”.  Now in 2018, GDPR and “Responsible Data” and “Blockchain” are trending – people want to know about it so they can talk about it.  While “trends” aren’t all we should be focusing on, they can often be the hook that gets people at all levels of our sector interested in taking their first steps forward in data literacy.

DATA LITERACY MEANS SOMETHING DIFFERENT FOR EACH PERSON

Data literacy means something completely different depending on who you are, your perspective within a programme, and what you use data for.

To the beneficiary of a programme…

data literacy might just mean understanding why data is being collected and what it is being used for.  It means having the knowledge and power to give and withhold consent appropriately.

To a project manager…

data literacy might mean understanding indicator targets, progress, and the calculations behind those numbers, in addition to how different datasets relate to one another in a complex setting.  Managers need to understand how data is coming together so that they can ask intelligent questions about their programme dashboards.

To an M&E officer…

data literacy might mean an understanding of statistical methods, random selection methodologies, how significant a result may be, and how to interpret results of indicator calculations.  They may need to understand uncertainty within their data and be able to explain this easily to others.

To the Information Management team…

data literacy might mean understanding how to translate programme calculations into computer code.  They may need to create data collection or data analysis or data visualization tools with an easy-to-understand user-interface.  They may ultimately be relied upon to ensure the correctness of the final “number” or the final “product”.

To the data scientist…

data literacy might mean understanding some very complex statistical calculations, using computer languages and statistical packages to find trends, insights, and predictive capabilities within datasets.

To the management team…

data literacy might mean being able to use data results (graphs, charts, dashboards) to explain needs, results, and impact in order to convince and persuade. Using data in proposals to give a good basis for why a programme should exist or using data to explain progress to the board of directors, or even as a basis for why a new programme should start up….or close down.

To the donor…

data literacy might mean an understanding of a “good” needs assessment vs. a “poor one” in evaluating a project proposal, how to prioritize areas and amounts of funding, how to ask tough questions of an individual partner, how to be suspect of numbers that may be too good to be true, how to evaluate quality vs. quantity, or how to see areas of collaboration between multiple partners.  They need to use data to communicate international priorities to their own wider government, board, or citizens.

Use more precise wording

Data literacy means something different to everyone.  So this priority can be interpreted in many different ways depending on who you are.  Within your organization, frame this priority with a more precise wording.  Here are some examples:

  • Improve everyone’s ability to raise important questions based on data.
  • Let’s get better at discussing our data results.
  • Improve our leadership in communicating the meaning behind data.
  • Develop our skills in analyzing and using data to create an impact.
  • Improve our use of data to inform our decisions.

This blog article was based on a recent session at MERL Tech UK 2018.  Thanks to the many voices who contributed ideas.  I’ve put my own spin on them to create this article – so if you disagree, the ideas are mine.  And if you agree – kudos to the brilliant people at the conference!

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Register now for MERL Tech Jozi, August 1-2 or MERL Tech DC, September 6-7, 2018 if you’d like to join the discussions in person!

 

MERL Tech London: What’s Your Organisation’s Take on Data Literacy, Privacy and Ethics?

 It first appeared here on March 26th, 2018.

ICTs and data are increasingly being used for monitoring, evaluation, research and learning (MERL). MERL Tech London was an open space for practitioners, techies, researchers and decision makers to discuss their good and not so good experiences. This blogpost is a reflection of the debates that took place during the conference.

Is data literacy still a thing?

Data literacy is “the ability to consume for knowledge, produce coherently and think critically about data.” The perception of data literacy varies depending on the stakeholder’s needs. Being data literate for an M&E team, for example, means possessing statistics skills including collecting and combining large data sets. Program team requires different level of data literacy: the competence to carefully interpret and communicate meaningful stories using processed data (or information) to reach the target audiences.

Data literacy is – and will remain – a priority in development. The current debate is no longer about whether an organisation should use data or not. It’s rather how well the organisation can use data to achieve their objectives. Yet, organisation’s efforts are often concentrated in just one part of the information value chain, data collection. Data collection in itself is not the end goal. Data has to be processed into information and knowledge for making informed decisions and actions.

This doesn’t necessary imply that the decision making is purely based on data, nor that data can replace the role of decision makers. Quite the opposite: data-informed decision making strikes balance between expertise and information. It also takes data limitations into account. Nevertheless, one can’t become a data-informed organisation without being data literate.

What’s your organisation’s data strategy?

The journey of becoming a data-informed organisation can take some time. Poor data quality, duplication efforts and underinvestment are classic obstacles requiring a systematic solution (see Tweet). The commitment from senior management team should be secured for that. Data team has to be established. Staff members need access to relevant data platforms and training. More importantly, the organisation has to embrace the cultural change towards valuing evidence and acting on positive and negative findings

Marten Schoonman@mato74
Responsible data handling workgroup: mindmapping the relevant subjects @MERLTech

Organisations seek to balance between (data) demands and priorities. Some invest hundreds of thousands dollars for setting up a data team to articulate the organisation’s needs and priorities, as well as to mobilise technical support. A 3-5 years strategic plan is created to coordinate efforts between country offices.

Others take a more modest approach. They recruit few data scientists to support MERL activities of analysing particularly large amounts of project data. The data scientist role evolves along the project growth. In both cases, leadership is the key driver for shifting the culture towards becoming a data-informed organisation.

Should an organisation use certain data because it can?

The organisation working with data usually faces challenges around privacy, legality, ethics and grey areas, such as bias and power dynamics between data collectors and their target groups. The use of biometric data in humanitarian settings is an example where all these tensions collide. Biometric data, e.g. fingerprint, iris scan, facial recognition – is powerful, yet invasive. While proven beneficial, biometric data is vulnerable to data breach and misuse, e.g. profiling and tracking. The practice raises critical questions: does the target group, e.g. refugees, have the option to refuse handling over their sensitive personal data? If so, will they still be entitled to receive aid assistance? To what extent the target group is aware how their sensitive personal data will be used and shared, including in the unforeseen circumstances?

The people’s privacy, safety and security are main priorities in any data work. The organisation should uphold the highest standards and set an example. In those countries where regulatory frameworks are lagging behind data and technology, organisations shouldn’t abuse their power. When the risk of using a certain data outweighs the benefits, or in doubt, the organisation should take a pause and ask itself some necessary questions from the perspective of its target groups. Oxfam which dismissed – following two years of internal discussions and intensive research – the idea of using biometric data in any of their project should be seen as a positive example.

To conclude, the benefits of data can only be realised when an organisation enjoys visionary leadership, sufficient capacity and upholds its principles. No doubts, this is easier being said than done; it requires time and patience. All these efforts, however, are necessary for a high-achieving organisations.

More reading:

**Save the date for MERL Tech Jozi coming up on Aug 1-2!  Session ideas are due this Friday (March 31st).