The only constant is change and change is difficult for most of us - humans and organizations alike. However, change is necessary for growth and, when harnessed properly, leads us to being more efficient and maximizing our potential.
Having spent several years as an Organizational Change Management (OCM) consultant and even more time as a change agent, I have seen firsthand the difficulty that people and institutions have with change. Even those that recognize the need for change and that cerebrally want to take the change journey have great difficulty with actually doing it.
Let’s define some core terms so that we are all talking about the same things in the same way.
Formally, change management refers to a systematic approach to handling the transition or transformation of a set of (institutional) goals, processes or technologies. Organizational Change Management (OCM) normally refers to a framework for managing the impact of new (business or technology) processes, updates to organizational structure, or cultural shifts within an institution.
John Kotter’s 1996 book on “Leading Change” outlines one of the more widely-used Organizational Change Management models in use today. If this topic intrigues you, then Kotter’s 8-step change model is worth examining.
The purpose of change management is to implement strategies for effecting change, controlling change and helping people to adapt to change.
A change agent is a person or group that facilitates the change process in an organization. The change agent is viewed as that entity that motivates, inspires, catalyzes, and potentially leads the change process; in hopes of a positive outcome.
If you are the change agent for your group, team or organization, then there are five things you have to do to be effective.
1. Embrace The Resistance
The source of most of the angst when it comes to change management is people. People will be resistant. Know this. Appreciate this. Be comfortable with this. Then determine ways that you can slowly chip away at this resistance. First step is to identify your allies.
Find the long-standing employees who have some degree of influence in the organization and partner with them. When colleagues recognize that a long-standing team member is on Team Change, they will be more willing to accept the changes rather than oppose them.
2. Co-Create the Vision
Most change management books will highlight the importance of creating a powerful vision. This emphasis is warranted and the advice is sage. However, it is more effective to have leadership and other influencers collaboratively working with you to craft your desired end-state. The vision needs to be a co-creation; with everyone feeling like they contributed and own the end result. Your vision needs to be easily understandable, to inspire action and to focus attention.
Frequent and consistent communication of the vision is one of the key strategies that will help you further erode the resistance that you will face. You and your allies can never talk about the vision too much.
3. Get Buy-In
Getting people bought into the idea of changing is vital. Steps 1 and 2 would have helped you get your allies and leadership co-creators bought in. However, you not only have to launch an awareness and feedback campaign for all affected, you have to ensure that the most senior leader is on Team Change and spreading the same good news.
Change management initiatives have a very high probability of failure when the top leader is not on board. They have to be bought in, actively communicating the vision, and demonstrating with their actions that they are supportive and enabling the change.
Also colleagues that feel outside of the sphere of this cool, new change are more likely to exhibit even more steadfast resistance. This is why it is crucial for them to be heard and provide feedback on the path forward.
4. Create a Track Record
Once you have the vision in a solid state and there are enough people bought in, then it is time to create your execution plan.
Be mindful of time and deliverables in your action plan. People will not wait for nine months to see the effect of your plan. You need to produce, demonstrate and constantly share tangible products to the organization every one to three months.
This process will build the momentum, support and excitement for change that you will need to fuel the successful execution of your plan. It also reduces the resistance you will face as you move forward.
5. Make Change Normal
Not only do you have to embed the changes made on the path to the vision, but you have to take steps to make change management a normal part of work life. Identify and utilize the levers available to you and your allies that can gently nudge people to continuously question and improve.
For example, when doing efficiency evaluations of your HR team, a useful lever to ensure continuous improvement would be to have a mandatory step that forces the HR team to do a re-examination of current policies and procedures in order to determine current relevancy and potential optimizations.
In the end, change is not easy for most. Being a change agent means that you will force those around you to think more critically and hopefully re-evaluate their existing behavior and ways of doing things. Given that most people become set in their ways after a certain period of time, this will be extremely difficult (and near impossible) for some. As a change agent, you have to be okay with this.
If you are brought into an organization as a change agent, be honest with yourself and know that the probability of you being in that organization to see the effects of your fully executed plan is pretty low.
If you are change agent with a long track record within an organization and a good reputation, recognize that steady and deliberate progress towards the end goal is the approach that will likely yield the most successful outcome.
Whatever your situation as a change agent, know that it is hard and important work and that the world needs you to keep going.
This post is also published on GovLoop.
No one will argue against the statement “data is important”. The proper use of data can make you and your organization very successful. Being aware of the areas that need to be improved and the areas that your customers love is a good thing. If you ignore the signal in your data, you risk seeing your operations and your products wither away before your eyes.
Data can be your ally and it is now widely recognized as the most important asset that any organization, public or private, possesses. However, we need more leaders with the ability to shepherd the good and virtuous process of executing on a data mission.
So, how do you become a Data Leader?
When I say Data Leader, I am not referring to having the title of Chief Data Officer, Chief Data Scientist, Chief Data Evangelist, Chief Data Strategist, etc. I am talking about cultivating and developing the traits that enable you to function in that capacity for your team.
As someone who had the honor of being amongst the first wave of Chief Data executives in the Federal government, and who achieved success in the role, I want to share the lessons learned that will get you on the path to being a Data Leader.
Current expectations are that a Chief Data Executive should be a technologist, a developer (scoping, implementing, and transitioning data products and services), a steward (for improving data quality), an evangelist (for data sharing and novel data business model generation), and a strategic visionary (for the organization’s data assets).
It is impossible for a single person to be all these things and accomplish them all in a standard work week. Thus, it becomes critically important that as a leader you are excellent at “managing by influence” . This means that you have developed relationships, where you can guide and work with other teams to execute on a common data mission - even though some team members do not report to you.
Influence is the cornerstone of the collaborations that are necessary to achieve escape velocity, i.e. the rapid stream of quick wins needed to build excitement and buy-in, and then to have long-term success and sustain it.
Building alliances is key to successful executing on your data mission. Generally, you cannot do it alone and your team cannot do it alone. You have to develop connections with the other parties that play a part in the mission’s execution.
In order for these alliances to be meaningful, your colleagues must have trust in you with regards to your word, and with regards to your moral compass and values. A Data Leader whose actions and or words are not grounded in integrity and cannot be relied upon will have a hard time achieving and maintaining the relationships necessary for any sort of success.
It is time to start demonstrating those values and building your reputation.
In this context, competence refers to “having sufficient skill, knowledge, and experience to perform the job”, i.e. being properly qualified. The common set of skills that are required to be a Data Leader include knowledge of the business and mission, knowledge of computer science, data science, or both, and knowledge of product definition and delivery. A competent Data Leader is a rare mix of technical guru, businessperson, marketer, and adept executive — someone able to communicate in all spheres and that can easily translate between each.
For some, it may be time to adjust your personal learning plans to include a few competence requirements for Data Leadership.
Even though many speak of the rise of the Chief Data Officer and the new damn of the suite of Chief Data Executives, many organizations and employees are still struggling to understand what these Chief Data Executives do, where they fit into the organization, what their essential skills should be, what these executives are responsible for, who they should report into, and how to measure their impact. I am confident that this will get sorted in due time.
However, the untapped and unrecognized gem in this entire scenario is the realization that these Chief Data Executives are harbingers of what is the come - a future where every team has at least one Data Leader who is performing the duties of a Chief Data Executive at the local level.
This post was also posted on GovLoop.
“Data is the new black.”
“Data is the currency of the future.”
“Data is the most valuable asset your organization has.”
“Is your organization data-driven?”
If you have heard any of these statements or questions, then you have probably wrestled with the issue of creating a data-driven culture.
Having a data-driven culture means that data is the fundamental building block of your team. It means that every team member has a data-driven mindset. It means that every single decision maker uses data as their main evaluation asset. It means that every project uses, generates and pivots on data. It means that your team is constantly leveraging data as a strategic asset.
But how do you get there?
Creating a data-driven culture depends on cultivating a mindset of experimentation, having the right infrastructure in place and developing the skills to interpret the signal from the data, while ignoring the noise in it.
For each team member, there are four steps that they must take on the path to becoming data-driven.
Step 1: Do You Know Where You're Going to?
The first step is to know the questions that you are trying to answer with data.
With unlimited resources, you and your team could monitor and store every single bit of data that you generate, that you use for your mission, or that you think may be relevant in executing your strategic objectives. Unfortunately, this is a very expensive proposition and you are not guaranteed that the data you have in your possession will be helpful.
Similar to other activities that must function under constraints and within resource budgets, defining the end state is extremely important.
The questions you want to answer with data provide the needed focus for data-driven success. Do you want to make a process more efficient? Do you want to decrease the time taken to successfully complete a transaction? Do you want to increase the number of customers that you can serve?
In your context, the questions that you want to ask of the data determine the data that needs to be collected. Yes, this is an obvious statement. However, it needs to be explicitly stated.
Know what you want to get answered from the data, then figure out the specific data items that needs to be collected and stored.
Be firm in clearly defining the data items, the units used and the meanings of each data point. Standardization and consistency will be essential when it comes to implementation and scaling your data infrastructure.
Step 2: Do You Know Whom You're Going to?
Once you know how you will interrogate the data, which helps you define what you should collect, you now need to understand the audience that this data will be presented to. Will the decision maker be yourself, a data scientist, an executive or your grandmother? This knowledge will help you determine both the transformations that need to happen to your source data and the correct visualization to use for maximum impact.
Knowing your intended audience also forces you start thinking about the actions that you want them to take when you present the data to them. When they see the visualized data, should they take out steps from a process? Should they increase the number of staff members on a particular task? Should they start more closely monitoring a specific business area?
Step 3: Do You Know How You're Getting There?
The hard part is mostly over. Now it is time to design and implement your ETL (Extract-Transform-Load) pipeline. Essentially, this is where you create the process and supporting mechanisms (technical or otherwise) that allow you to get data from the desired data source, cleanse and massage it into the right form with the right semantics, and then store it in the data management system of your choosing.
Start small. Pilot your ETL with your simplest use case. When you have it working satisfactorily, expand the scope of scenarios that your ETL pipeline can handle until it covers all of your needs.
Step 4: Do They Know What They're Looking at?
In the end, the presentation and the interpretation of the data is what decision makers interact with and what facilitates the creation of a data-driven organization. A choropleth map with multiple data variables on it may mean nothing to your boss if they don’t intuitively understand both the visualization type and the message that is trying to be conveyed.
For this reason, it is critical to use information you gathered in Step 2 to create the right visualization for your audience, which should lead them to interpret the data in the right way and make the right decisions.
A word to the wise. Data is a reflection of the world around us. Unfortunately, the world around us is flawed and has deep systemic problems. So, be careful in your quest to be data-driven. Be careful in your exclusive use and trust of data.
It is better to be informed by data rather than only data-driven.
This was also posted on GovLoop.
How do I scale my work? I hear this question in many different ways, in many different venues and many different times a day. I hear it from startups that are trying to figure out how to become a “real business." I hear it from government employees that want to figure out how to turn their homegrown innovation into an agency-wide asset. I hear it from companies trying to get their product to support a significantly larger number of clients. I hear it from hobbyists trying to maintain the spirit of their side project, while trying to evolve and grow its functionality and not upset its early adopters. I hear it a lot. However, at the core, it is the same question. How do I increase the impact of my work?
Everyone that talks about scaling should understand that they are referring to the ability of their work – whether a system, a tool or some other innovation – to cope and perform under an increased or expanded workload.
Something that scales well will be able to maintain, or even increase, its performance or efficiency when tested by larger operational demands. If I am a patent reviewer and I created software that helps get synonyms for my patent search in a tenth of the time it normally takes, then scaling could mean how I extend this software to cover more patent areas and be able to handle more than one person using it.
This particular topic is not addressing “operating at scale,” “scaling a business,” or “scaling a team." However, elements from this discussion can be applied to those topics as well.
In my experience, there are typically five steps needed to scale one’s work.
Warning to all the non-techie types, a lot of this comes from my experience as a software engineer, software development manager and product manager. The good news is that despite this warning, the insight is applicable to you and your field:
STEP 1: KNOW THYSELF
The first thing that you must do is to be clear about your work. Articulate its purpose and its main contribution. Define the core competency of your innovation. Specify the attributes that make it valuable. Specify, from the perspective of someone using or consuming your work, the things that differentiate it from other contemporary solutions. This is the starting point for discussions with your advocates, champions, approvers and those that will help you with this scaling effort. Needless to say, it is also the starting point for creating a scalable version of your work.
STEP 2: BREAK DOWN AND STANDARDIZE
Now that you have put in the work to get to the core of what your innovation does, break down the entire system into a set of simple and logical components. Identify all the interactions between your components, eliminate as many manual elements as possible and automate all the logical elements that are repeatable tasks. There is a lot of value in the church of KISS (Keep It Simple Stupid).
For scale, you have to ensure that your work performs consistently, irrespective of workload. Thus, standardizing and defining workflows and operations is critical. The last task in this step is to document the way your system works, the way it is deployed and the way it should be used. This documentation is a useful blueprint that will help when it comes to getting traction.
STEP 3: BE CLEAR ABOUT YOUR GOAL
I have seen too many people who recognize that they need to scale, but don’t refine the goal any further. Do you want to increase your number of users by a factor of 10 or 100? Do you want to increase the services your product offers? Do you want to increase the coverage of your tool? Do you want to increase your revenue tenfold? Getting clear on the dimension that you are optimizing on, on the metric being used, and on the target value for your metric(s) will provide the focus needed to guide you to a successful strategy, and associated tactical actions.
STEP 4: KNOW YOUR TARGET AUDIENCEYour audience, both users and stakeholders, is first and foremost the feedback mechanism that tells you if you’re heading in the right direction or not. Understanding your users’ behavior and their interactions with your work will be pivotal in determining the successful approaches to take to the land of scale.
STEP 5: EXPERIMENT, TEST, ITERATE
A lot of founders tend to believe that they can achieve scale by doing more of “the same." Their rationale is that the actions that got me here made me successful, so more of the same should be good enough.
Unfortunately, a solution built for a few thousand people will not be the same solution that is needed for a few million people, even if the core function remains the same. This is why we go through steps 1 through 4 first. They help us to figure out the essential aspects of our work and the aspects that may need to evolve.
Once you realize that what got you here won’t get you there (to your goal), you can start to appreciate the need to experiment with all the assumptions, components and processes that underpin your solution.
View this as a growth hacking experience. A growth hacker conducts multiple experiments across marketing channels, product development, sales segments and other areas of a business to identify the most efficient ways to grow a business.
This should be your mindset, working with your users and (potential) advocates to determine the best way your work can accommodate the needs of your agency, community or organization. This involves creating experiments, testing them and using the feedback to improve your work.
At the end of the day, the core function of your work will remain the same. However, expect a lot of change as you scale it to handle more, and also fit within a new environment. It is nothing to be afraid of. Embrace it. You got here because you were successful.
This post was also published on GovLoop.
For the past two decades, I have been consumed with positive impact. How do I leave the world better? How do I do great things that help? How do I help teams do great things that help?
When I started my career, the focus was on how my individual contributions could produce this change. As time progressed and my responsibilities increased, the focus moved to how I could create an environment for my team(s) to do their best work.
Years in the trenches - working, managing, leading, coaching, mentoring, reading, applying, learning and incorporating lessons - have surfaced four principles that are common to the highest-producing, highest-functioning teams that I have been a part of.
Lack of trust between team members is a solid indicator that your team, company or agency will be operating from a place of fear, will be high stress, will contain lots of isolated teams and people, will have high turnover and will not be performing as efficiently as it should be.
A “high trust” environment is fundamental and a critical building block for a great team. This environment provides a team with the psychological safety that is needed to create a high-performance, high-retention, collaborative and productive space.
For leaders who want to create a team with high trust as its core, the strategy must include: 1) ensuring that relationship building is a natural, everyday part of people’s work life; 2) enforcing equal airtime for team members during meetings; 3) emphasizing that every team member should assume good intent for each action they observe or experience; and 4) encouraging the vocalizing, vetting and supporting of all the ideas brought forward by team members.
From my experience, taking these steps will normally create a more cohesive, happier, more loyal and more fulfilled team. Productivity normally increases, turnover decreases and teamwork strengthens.
The more dominant (and popular) management techniques that I have seen being commonly used by leaders tend to emphasize the chain of command, information layers and the filtering of context and data about projects and goals down the chain.
Unfortunately, this creates an environment where team members are operating with half truths and missing information. For successful execution of your team’s mission, this raises a set of interesting problems that actively work against the team being successful.
A “high truth” environment is built on transparency, realistic expectation setting, facilitating and having difficult conversations and giving and receiving constructive criticism well. A team member who knows the reality of the mission, the project, how the project contributes to the mission, the actual constraints of the project and the actual business, legal, technical and social factors that shape the project will have a higher probability of making great decisions. This will also help with delivery that is on time, in scope, under budget and embraced by happy and satisfied customers.
Unfortunately, the groundwork for leaders to build this environment involves dealing with messy human emotions and concepts that most of us are trained to avoid. Fortunately, the Harvard Business Journal has a cache of valuable publications that help you take the steps necessary.
Empathy is the ability to see and understand a situation that you are involved with from the perspectives of the other people in the situation. My experience is that “high-empathy” colleagues normally lead to collaborations and customer products that are well-received, joyful and successful.
Ever work with someone that rubs everyone on the team the wrong way? Ever have a teammate that only understands, advocates and pushes their perspective or agenda? Ever interact with a coworker that immediately defaults to an us-against-them mentally? If you have, then you had the pleasure of working with a “low empathy” individual.
Generally, the best course of action for leaders is to hire for “high empathy.” If this is not possible and you have team members that need to evolve as human beings on the empathy front, then a leader has a lot of customized and individualized coaching and mentoring to perform with these colleagues.
There is nothing worse than working for an organization with a vague mission, a vague vision and even vaguer project definitions. Though some flexibility is often necessary in software development projects, flexibility should and can be built into goals that are clear.
Lack of clarity allows every level of management and every team member to interpret goals the way they want to. Given that everyone has their own unique story and journey means that there will be multiple viewpoints of what should get done and multiple people doing very different misaligned things. They might also claim that they are synergistically working towards the same outcome.
Leaders can and should detect these misinterpretation issues and take measures to provide clarity and define reality using a common set of agreed-upon fundamentals around tasks and goals. The series of interactions that are necessary are also great ways to enable crucial relationship building that is necessary for high trust environments.
Over the last five years, Google’s quest to build a perfect team has codified and put formal names to the experiences I have had. Their study, available here, is a great read for someone who wants to go deeper.
Building great teams that produce great work is difficult, but a necessity in today’s world. Through a series of failures and hard life lessons, I have seen the importance of trust, truth, empathy and clarity. I have used these concepts on each team that I have either created or been a part of, over the last decade, to great result. I hope would they help you.
This post was also published on GovLoop.
The year is 2020 and NASA's Planetary Defense Coordination Office (yes, it's real) has notified the world that an Extinction Level Event (ELE) - let's say a series of asteroids moving quickly on a collision course - is projected to wipe out the Earth in three months. What should our response strategy be?
Unfortunately, neither Options 1 or 2 get you to a level of understanding of the root cause of the issue and instead of solving the problem, you most likely end up addressing a symptom.
For Option 1, the possibility exists that the asteroids you destroy are a sign of things to come and that there are bigger, faster, weird-behaving masses right behind them. So, taking this approach may only buy us time.
For Option 2, because there are no early detection systems on this new planet, we could be buying a few more months (as other asteroids are barreling towards this new planet), we could be getting humanity out of harm's way, or we may be putting the human race in a worst situation (if there are structural issues that make the new planet a ticking time bomb).
Option 3 - determining the root cause of the situation - requires calm, focused, objectivity on the What, Why? Where? Who? When? and How? Filling out the scenario with this information not only guides us to the correct path to take, but helps lessen the chances of unforeseen and unexpected subsequent issues. It is well-known insight in computer science that formulating the problem correctly is far more valuable than rushing to a solution based on surface facts about the customer's pain points. However, it has become obvious that far too few people spend time trying to determine the root cause of a problem.
Over the past month, I have been reading an increasing number of articles promoting solutions to issues that simply put a controlled solution box around a symptom, rather than solve the real problem.
Let's take two areas: the refugee crisis and the diversity crisis in tech.
Here is the existing narrative with regards to the Refugee Crisis:
Global migration has reached an unprecedented scale. Millions of people cross borders every year in search of new opportunities, carrying with them enormous potential to contribute to economic development, address demographic challenges, and foster global interconnectedness. But global migration also comes with pressing challenges. Many migrants undertake perilous journeys only to be exploited or face deportation. Women and children are illegally trafficked across international borders and sold into slavery. Even legal migrants are facing a rising tide of xenophobic backlash. Global refugee numbers are continuously rising as civil wars and conflicts rage on. Climate disasters and changing environment will further cause the displacement of hundreds of thousands of people.
The current approaches to solving this issue revolves around answering the following questions:
But what is the root cause of this crisis?
Political unrest, civil war, and gentrification stem from unbalanced and unjust economic models and incentive schemes that promote discord, strife and conflict at the micro level in service of (personal) profitability for a select few at the macro level. This inequality and the inability of a few to empathize with others and view them as a part of their family drives the conditions that produce environments where the weak and powerless have to flee; in hopes of increased safety and the possibility of better opportunities.
Much of this knowledge and nuance is hidden from most. Much of this activity is (covertly) performed by the elected representatives and wealthy citizens of the countries that refugees migrate to. The public tends to be blissfully unaware. With this knowledge hidden, the general populous is provided with a narrative that paints a picture of a distant problem, in a distant land, involving “others” and completely divorced from themselves, leading to an influx of people that want to drain their resources, take what is theirs, and erode their quality of life and their way of living.
The critical first steps in solving this is with education, increased awareness, and empowerment.
Educating powerful stakeholders that there are fruitful and lucrative alternatives, which are better for everyone in the long run. Educating the general public on their role, whether knowingly or unknowingly, in creating these crises. Educating the residents of the country that refugees are migrating to on empathy, on the benefits of immigrants, and on demystifying the fears and myths that they hold. Educating the world that addressing the refugee crisis is about more than crafting solutions around the consequences of the problem, but rather creating fundamental and positive changes that address the sources of the issue.
Diversity in Tech
Sad to say that since I wrote this piece in June of 2015, it is still relevant and the majority of people don't seem to get it - systemic racism is the root cause for the lack of diversity in computer science.
Apple's diversity number haven't changed (more here). Companies are still holding talking sessions between white male and white female leaders on Diversity in Tech (more here). The narrative of it being bigger than race, that we should take incremental steps, and start with white women first is still being pushed (more here, here, here, and here). Accelerators, social entrepreneurs, and social venture capitalists are still being heralded as "the path" forward (more here and here).
It appears that it is too difficult for people to be thoughtful, introspective, and to objectively name the root cause - a system built to ensure that one community succeeds at all costs and at the detriment of all other communities. The system needs to be changed to be fair and equitable for all, and its "in-power" community members need to re-socialized.
Without this crucial first step, we are putting bandaids on the symptoms and not fixing the root cause. We can create solution layers on top, but still won't stop managers from discriminating and hiding it. These symptom solutions will only lead to new and interesting manifestations of the root cause.
Please re-read "Real Talk About Diversity in Tech" and be real with me and yourself as to the problems we need to be looking at.
Where are you seeing people addressing the symptom and not the root cause
I just tried to load the website for the Commerce Data Service (http://www.commerce.gov/dataservice) and it redirected to http://www.commerce.gov, which means that the Service is officially dead. I have no idea when it was officially shut down. However, the last snapshot from the Way Back Machine was on July 31st, 2017.
It kinda hurt. I had a moment.
The startup that Jeff Chen and I founded and poured 100+ hour weeks into is no more.
Rather than be sad, it is time to celebrate.
As a team, we ran fast and we ran far.
We created a program to teach Commerce employees human-centered design, agile software development, and data science. A program that is now a model adopted and used by other Federal agencies.
We worked side-by-side with Commerce agencies to deliver over twenty solutions that improve their efficiency and highlight a new way to engage with vendors, staff, and the American public.
We helped to spotlight and engage the private sector and everyday Americans on the power of the data that Commerce collects and makes available for free -- addressing everything from income inequality, to access to opportunity, to the school-to-prison pipeline for minority girls and women, to intellectual property innovation.
We moved the needle on getting the technical infrastructure of the Department more aligned to best practices in today's information economy.
The world outside of Commerce saw the work & its impact and publicly recognized the team through numerous awards.
It was the best of times. It was the worst of times. Mostly, it was a helluva time.
Cheers, to the Commerce Data Service (CDS) team.
For more information on the CDS, see:
Today (September 25th, 2017), the Institute for Health Metrics and Evaluation (IHME) is pleased to release the Health Atlas mobile application (formally "Health Atlas by IHME") - a mobile app, available on Android and Apple devices, that provides country-level statistics from the Global Burden of Disease (GBD).
You can explore country-level stats on over 200 countries, compare trends for each country from 1990 to 2016, determine which diseases lead to the most loss of healthy life, share interesting findings with your friends and co-workers on social networks and other platforms, customize and filter the data displayed in each graph, easily copy and embed graphs into emails, texts, documents and presentations, and discover leading causes of death and injury by gender, age group, and geography.
You can currently access the app's data in English, Chinese, Spanish and Russian.
If you have feedback or ideas for improvement, send your thoughts to email@example.com.
More information is available at http://www.healthdata.org/healthatlas.
At 3:30pm today (September 14th, 2017), the Institute for Health Metrics and Evaluation (IHME) launched the 2016 Global Burden of Disease (GBD) - a comprehensive data set on the risks, injuries and diseases that impact the number of healthy years due to various factors; across gender, age groups, and time.
GBD Compare (https://vizhub.healthdata.org/gbd-compare/) now contains the data from the 2016 round of analysis.
You can now analyze updated data about the world's health levels and trends from 1990 to 2016 in this interactive tool. Use treemaps, maps, arrow diagrams, and other charts to compare causes and risks within a country, compare countries within regions or the world, and explore patterns and trends by country, age, and gender. Drill from a global view into specific details.
Compare expected and observed trends. Watch how disease patterns have changed over time. See which causes of death and disability are having more impact and which are waning.
You can download the raw data, for non-commercial purposes, using the GBD Results Tool(http://ghdx.healthdata.org/gbd-results-tool).
You can use the Data Sources Tool (http://ghdx.healthdata.org/gbd-2016/data-input-sources) to explore the input data used in creating the 2016 Global Burden of Disease.
If you want to browse, reuse, or improve the code used in the production of the GBD, it is publicly available on Github (https://github.com/ihmeuw/ihme-modeling).
(This post is available on LinkedIn)
Data is now widely recognized as the most important asset that any company, public sector or private entity, possesses.
The latent power of data and recent technological advances, in the production and utilization of insight gained from extremely large volumes of varied, multi-modal and high-frequency data sets, has led to the recent rise of a new class of professional roles — Chief Data Officers, Chief Data Scientists, Chief Data Evangelists, Chief Data Strategists, etc.
It has been about 5 years since these roles have become “hot jobs”.
As someone who had the honor of being amongst the first wave of these professionals in the US Federal government and who achieved success in the role (read about my time as the Deputy Chief Data Officer for the US Department of Commerce here and see what we accomplished here), I want to share the lessons learned from my service.
From my experiences, effectiveness as a Chief Data Executive hinges on three critical factors:
Current expectations are that a Chief Data Executive should be a technologist, a developer (scoping, implementing, and transitioning data products and services), a steward (for improving data quality), an evangelist (for data sharing and novel data business model generation), and a strategic visionary (for the organization’s data assets).
It is impossible for a single person to be all these things and accomplish them all in any given work week. Thus, it becomes critically important that the Chief Data Executive is an excellent “manager by influence” — able to synergistically guide and work with other teams to execute on the data mission. This influence is the cornerstone of the collaborations necessary to achieve escape velocity and then have long-term success and sustainability.
Building these alliances, which lead to a pathway of success, is built upon the trust that one’s colleagues must have in you with regards to your word, and with regards to your moral compass and values. A Chief Data Executive whose actions and or words are not grounded in integrity will have a hard time achieving and maintaining the relationships necessary for any sort of success.
In this context, competence refers to “having sufficient skill, knowledge, and experience to perform the job”, i.e. being properly qualified. The common set of skills that are required to be a Chief Data Executive include knowledge of the business and mission, knowledge of computer science, data science, or both, and knowledge of product definition and delivery.
A competent Chief Data Executive is a rare mix of technical guru, businessperson, marketer, and adept executive leader — someone able to communicate in all spheres and that can easily translate between each.
At this moment in time, many organizations and employees are still struggling to understand what Chief Data Executives do, where they fit into the organization, what their essential skills should be (based on their needs), what these executives are responsible for, who they should report into, and how to measure their impact.
But these are all topics for another time. :-)
(This post is also available on Medium)
Dr Tyrone Grandison
Executive. Technologist. Change Agent. Computer Scientist. Data Nerd. Privacy and Security Geek.