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SEO techniques are supposed to help a website get more traffic from the target audience. One can spend plenty of money on hiring an SEO consultant. But is the expense worth the cost? We explain this by telling you about 10 tricks SEO experts use. Social media plays an important role as we explained previously here:

Read this entry in German here.

How can I save the cost for an SEO consultant?

When I got another question from a client in Europe 2 weeks ago I decided to put these 10 tips together to help us all save some serious cash.

Recently somebody in Europe asked me something similar to

Why should I not hire an SEO consultant?

Client asking drkpi®

The question is a good one since you can spend quite a lot of money hiring an an SEO consultant. The short answer we gave is below

Just follow our 10 tipps for avoiding the necessity for hiring an SEO consultant.

drkpi®

Download the checklist: Quick SEO Check: Tips and Tricks?

Traffic from organic search results experienced a sharp downturn at the end of March 2020. But so did Google’s search engine advertising. Nevertheless, during later April, things have again improved. Ruth Porat (Chief Financial Officer) pointed out that Google had seen “some signs users are returning to more normal behavior in search.” Hence, SEO techniques are again very important to get the traffic you need to succeed after the Corona lockdown.

Which SEO techniques are popular? 10 secrets SEO experts won’t tell

Some of the things you will here is that great content is king or organic content (i.e. content you wrote yourself) adds value. Such content that helps your target audience is the type that answers questions, a guide on how to overcome a problem or a video explaining how to put together an IKEA bookshelf. We have put these together below:

Why should we refrain from hiring an SEO consultant? Many SEO consultants are not that much better than you are if that. Things like building 5,000 links for x dollars… that is a waste of time and money. Instead, spend it on writing organic content that adds value and solves your target’ audience’s problems.

Why do social accounts in our brand name matter? You want to own the brand name or your URL in the “eyes” of Google. Thus a social media page for your brand or company is a must. There are many more than just Twitter, Facebook, Instagram, WeChat, … So for the main ones, do it yourself and post 1 x a week if at all possible or 3 times between Monday to Friday on LinkedIn.
You can also hire a person to open 50 brand pages for you for the price of about US $ 3 to 10.
PS: but you still have to add content… :-) So choose wisely. Get a guy to do it (click)

How often should we update our website and blog? In the beginning, 2 x a week might be ideal. Thereafter, use 2 x 4 times a month. Sometimes you can simply not afford to spend more than 5 to 10 hours to create more than one blog post or webpage entry a month.

Why should we write so-called White Papers? These can be checklists, research reports or more extensive things. Allow people to download these papers. For instance, create a landing page / Wiki for a topic with many white papers, checklists, guides to download.

Which Google Tools should we use? Use the free Google tools – Google Analytics, Google Business Page (your entry is placed prominently in organic search results when people search for your company or brand name), Google Search Console, Google Scholar (research results), Google Question Hub and Google Trends.
PS. Comparing yourself to and mentioning your competitor’s name or brand will very likely bring you some of their

Why is the structure of a website important? Google looks for content that it beliefs users can understand. Accordingly, it looks for content that users can scan on their smartphone. Headers in html code such as H1, H2 and so forth in the text as well as bullets and short paragraphs/sentences help. Headings or sub-titels (e.g., H3) that ask a question that you answer in a few sentences right below is what Google is looking for (see also point 9 below).

Why is it worthwhile to talk about one’s competition? Start-up should talk about competition – it is maybe a bit touchy. Nevertheless, comparing your product to the competition helps attract potential customers and tells them why your product is a viable alternative.
PS. Comparing yourself to and mentioning your competitor’s name or brand will very likely bring you some of their traffic from Google searches.

Why do conversations help with Google? Links are like votes, they indicate to Google that your content is important. Moreover, if you link to content on the corporate website from a comment you write on LinkedIn, it also permits you to gain targeted traffic.
PS. Never buy backlinks from websites or social media channels. Google does not reward you for it. It wants links from websites where the content that links to your website is relevant (e.g., an SEO entry links to this entry about 10 SEO tricks experts will not tell). Similar to Likes or Shares, Google interprets backlinks from quality site as confirming that what you do is worthwhile.

How can Google questions and answers help? You should make use of Google questions and answers – just use your search window and type them in – also see Google Question Hub and sign up right now.
PS. Make it easy for Google to understand (point 6 – structure). Use a question as a heading for your page or blog entry (H1) or an H3 type header, followed by text answering the question.

How can you build on what content you have already? Don’t worry too much about design, perfect font and so forth. In other words, websites get constantly improved upon. Do not wait until things are perfect, do a soft launch (just launch and tell a few people). Work right now with what you have, preferably today not tomorrow.
PS. In the internet age, launching beta software, tools and websites has become the norm think. Tools get released before they are 100% done, except for medical technology or a Corona vaccine, of course.

SEO techniques: Final things to keep in mind

SEO techniques are often simplified by focusing on keywords. But a list of 100 keywords is as useless as one with 5 keywords that you fail to use in your texts or videos.

If you have your 10 keywords or thereabouts, you have it pretty much covered. For us these are such as data protection, GDPR, content marketing, digital marketing, analytics, social media audit, website audit, search engine optimisation audit, brand buzz, metrics and technology management. But now you must make sure that these words are used in your website content or blog as well as white papers. That is a never ending marathon that you need to take care of.

Besides keywords and using them in text, you need to continuously focus on updating your social media accounts, updating your website, commenting on other relevant websites and so forth (see our 10 tips and tricks above).

If you follow our 10 tips and tricks, you should be well on your way to receive more targeted traffic to your website.

Where do you want to go, reflection is needed.

[su_highlight background=”#fffe99″]Summary[/su_highlight]: David Cameron knows that public approval of RAF air strikes against ISIS in Syria has dropped.
We explain what this teaches Migros, Lidl and Tesco about new product research.

CLICK - CONFIDENCE in measuring ROI of social media and display ads is LOW

Some weeks ago I came across a report (see image) that stated just 29 percent of people feel confident in measuring the ROI (return on investment) of display ads and this drops to just 22 percent for social media marketing.

Accordingly, management is interested in improving its understanding with analyses and analytics when it comes to social media activities. But do managers or politicians understand what we are trying to communicate or convey to them?

If managers read blog entries like this one about how to do surveys, it’s no surprise that they believe it is all easy and cheap to do.

This is the fifth post in a series of entries about big data. Others so far are:

Data analytics: Lessons learned from Ebola
Scottish referendum: A false sense of precision?
– Facebook mood study: Why we should be worried!
– Secrets of analytics 1: UPS or Apple?

Confusion abounds

How are management or politicians supposed to understand the difference between analytics, data and analysis? Can we trust polls or should we learn from the Scottish disaster?

For instance, when we go to a dictionary of statistics and methodology from 1993 (Paul Vogt), neither analytics nor business analytics has an entry, never mind data analysis.

Kuhn: Unless we share a vocabulary, we are not a discipline

However, these days, some would claim data analytics is a science (e.g., Margaret Rouse). Still, if something can be called a science (e.g., physics or neuropsychology), its members share a certain set of beliefs, techniques and values (Gattiker 1990, p. 258).

Do people in data analytics or data analysis share a vocabulary and agree to the meaning of basic terms? Not that I am aware of. Therefore, Thomas Kuhn’s (1970) verdict would be: Not a science (yet).

In web analytics, data analytics or data science as well as social media marketing we agree to disagree. But maybe I can clarify some things.

Sign up for our newsletter; this post is the first in a series of entries on business analysis and analytics.

[su_box title=”2 things business, data, financial and web analytics have in common ” box_color=”#86bac5″ title_color=”#ffffff”]

1. All analytics is art that involves the methodical exploration of a set of data with emphasis on statistical analysis.

2. All analytics include the examination of qualitative and quantitative data.

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Analytics gives you the numbers, but fails to provide you with insights. For that, we must move from analytics to analysis, and we only gain the necessary insights if we do the analysis correctly.

[su_custom_gallery source=”media: 2649″ limit=”7″ link=”image” target=”blank” width=”508px” height=”552px” Title=”Diagram: Analysis versus Analytics versus Data – why the difference matters” alt=”Diagram: Analysis versus Analytics versus Data – why the difference matters”]

The graphic above illustrates that proper data is the foundation for doing analytics that permit a thorough analysis. Accordingly, using a sample that is not representative of our potential clients or voters is risky.

Nobody would draw any conclusions about attendance at next season’s football matches by asking a sample of baseball afficionados. So, go ahead and ask your social media platform users to vote for this season’s favourite flavoured drink syrup. But such a poll won’t give you an answer that is representative of your customer base.

Nevertheless, this is exactly what Migros did in 2015 (see Migipedia – few very young users participated in the poll, less than 10 wrote a comment during January 2015). It then published a one-page ad (among many more, see below) in its weekly newspaper (e.g., November 30, 2015), claiming that the chai flavour was the winner.

Making such a decision based on this type of unrepresentative poll is a risky choice. You may actually choose to increase production of the wrong flavour!

[su_custom_gallery source=”media: 2781″ limit=”7″ link=”image” target=”blank” width=”520px” height=”293px” Title=”Polling online community members gives you data from a non-representative sample of your customers – is that good enough to launch a new product?” alt=”Polling online community members gives you data from a non-representative sample of your customers – is that good enough to launch a new product?”]

Collecting data that is based on a representative sample of your customers is a costly exercise.

So why not use your online ‘community’ to do a ‘quick and dirty’ poll?

Surely a Twitter, Facebook or website / corporate blog poll is economical. You do it fast and easy and voilà, you got what you need, right? NOT.

Okay agreed, doing the above will strengthen your hand with a CEO. They might not grasp basic methodology issues of sampling or survey research. Plus, you got data from your online community, which is another reason to invest more money there.

In the Migros example above, having an online poll on your Migipedia platform achieves 3 things:

1. it allows your marketing folks and community managers to show the platform is useful for something;

2. regardless of which flavour wins and gets produced, you can always push it in your company newspaper. This way you reach 3 million readers in Switzerland – a country that has 7.8 million inhabitants,

3. even if the new product turns out to be a flop, thanks to other marketing channels, you sell 150,000 to 300,000 (or more) 1-liter bottles of chai tea syrup during the Christmas Season.

With its many resources and varied marketing channels (e.g., weekly Migros Magazin), Migros can ‘afford’ to use shabby research. It is in the enviable position to succeed, in spite of ‘spending’ so much.

The company might never learn that its analysis actually led the team to choose the second or even third best choice. Nonetheless, your marketing clout ensures that you can show it to management as an example of having done the right thing. Of course, we know it was done for the wrong reasons, but since management probably won’t find out, who cares – right?

[su_custom_gallery source=”media: 2793″ limit=”7″ link=”image” target=”blank” width=”530px” height=”308px” Title=”Polling: Opinion on RAF air strikes against ISIS in Syria – up and down each week” alt=”Polling: Opinion on RAF air strikes against ISIS in Syria – up and down each week”]

One poll is worse than none

As the above image from last week regarding air strikes in Syria shows, poll results can change quite a bit within a week.

For starters, no pollster wanting to stay in business will use a non-representative sample to get opinions. Using such data is unlikely to give you the insights you need for Hillary Clinton or any other candidate to succeed during next year’s US election.

[su_custom_gallery source=”media: 2801″ limit=”7″ link=”image” target=”blank” width=”485px” height=”445px” Title=”Polling: YouGov’s Will Dahlgreen never answered this question – so can you trust these results?” alt=”Polling: YouGov’s Will Dahlgreen never answered this question – so can you trust these results?”]

I left the above comment at the end of the blog post (it has not been published by YouGov so far). I asked about things that a good pollster will always publish with the poll results.

For instance, I asked how data were collected, whether the sample is representative, and what the margin of error was. I could not find any information about any of that. Of course, trust is not improved when one fails to publish a reader comment that raises method issues about your poll.

“YouGov draws a sub-sample of the panel that is representative of British adults in terms of age, gender, social class and type of newspaper (upmarket, mid-market, red-top, no newspaper), and invites this sub-sample to complete a survey.”

How exactly this happens with YouGov we do not know, since the methodology outlined on its website is not very detailed.

But David Cameron knows that while 5 million people have joined the ranks of those opposed to airstrikes in Syria in the past seven days, that could change next week. Polls are more interesting when they show a trend, so Mr Cameron still has hope that the opposition even more.
[su_box title=”5 key pointers for explaining the analyst’s work to your management: The case of survey research or polling” box_color=”#86bac5″ title_color=”#ffffff”]

Collecting quality data is followed by analytics, which subsequently require analysis to draw the proper insights. Analysis requires words in addition to looking at the numbers.

To tackle this challenge successfully, we need to do some preparation, as outlined below.

1. Do you have a strategy or a plan?

What is it you want to collect data for and why? This must be explained in a few sentences.

How will these data help you win the election, get the contract or sell more product?

2. How will data help you execute the plan?

You must know what data you need or the rationale for wanting them (see point 1).

What three steps will you take in the next quarter or six months to execute your strategy?

3. Are the numbers complete?

Most monitoring services can tell you everything about Facebook or Twitter.

But what about smaller websites from climate change activist groups, ISIS sympathesizers or peace activitists’ blogs?

Make sure you get the data you need. Is your sample representative of those whose opinion you must know?

4. Do you need social media monitoring?

Knowing what people say about your brand or company is a good thing. The Volkswagen emission scandal (remember #dieselgate) teaches us that in a crisis, simply monitoring the flood of tweets and status updates on Facebook or LinkedIn is of little use.

Like Volkswagen, you can decide to ignore the social media noise. Change your behaviour and communicate openly and directly (click for German-language radio report).

Unless you use social media monitoring to take action after the data are in, why collect it?

5. Do you have data from your customers?

If you have less than 1,000 employees, don’t make a big fuss about social media monitoring.

Focus on things that matter, such as what your clients report regarding warranty service, and the quality of phone support or user manuals. A tweet matters little.

Feedback can be collected in many ways, including customer surveys, discussions with clients or comments on your corporate blog.

Analysing these data provides insights that help improve product, service and so forth.

What it means

Focus on collecting data that help you serve your customers better. Getting a daily digest about the most important key words regarding your brand (e.g., we use DrKPI, #DrKPI, DrKPI BlogRank, #metrics #socbiz) is probably all you need. Instant data may not be needed unless you are a FT Global 500 company.

Restrict yourself to collecting only those data you absolutely and definitely must have.

Make sure that they meet some minimum quality standards. Only this will enable you to trust the analytics and analysis resulting from that work.

Actionable metrics are what matters

Unreliable or invalid data from clients, social media monitoring and opinion polls is a waste of resources.

Please keep in mind, just collecting data without taking action is a navel-gazing exercise.

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Bottom line

Always ensure that analytics leads to analysis that goes beyond navel-gazing metrics. Answer these questions truthfully:

A. What will be done with the findings: Unless you take action based on your data, why measure and collect information at all?

B. What kind of data was collected: Make sure you understand how data were collected. Can this polling data be trusted to be representative of the population (e.g., consumers in my country)?

How was something like influence (e.g., Klout) measured (what kind of proxy measure was used)?

If it is not transparent to you, move on and do not waste your time with such a measure or index.

Keep points A and B in mind before you collect data and / or use somebody else’s findings.

‘Total X’ combines xyz Labs’ proprietary Rambo social media measurement tool, and WalkBack®, the leading measurement source of WOM marketing from the Sambo Group, a Laughing Stock company.

Okay, what does the above mean? Who would want to trust this gobbledygook? If marketers or pollsters cannot explain things clearly and precisely, they tend to cover it up in jargon that tells you nothing.

Regardless, 2016 will mark the year where Lidl, Migros and Tesco will do more of these utterly useless polls, to find another ‘winner’ for a new flavour of drink syrup, mustard or soft drink.

Even though social media, community and marketing managers will claim a victory this year, with so much additional marketing around, who is surprised? Put differently, regardless which syrup the company – Migros – would have produced, I dare to claim it would have flown off the shelf anyway.

Combine all the ads and marketing push, and if it tastes okay, success is in the bag. Unfortunately, those that hate research will attribute part of this success to a useless online poll.

Next time you read something like the above, claiming to rank something, check the methodology. Cannot find anything? Just move on because it is probably hogwash.

Interesting reading

Vogt, Paul W. (1993). Dictionary of statistics and methodology. Newbury Park, CA: Sage Publications. For information see https://uk.sagepub.com/en-gb/eur/dictionary-of-statistics-methodology/book233364 (5th edition 2016).

2 great reading lists for additional resources about research, polls, survey data and much more:

1. http://guides.library.cornell.edu/c.php?g=31819&p=201525
2. http://www.lse.ac.uk/methodology/study/Preliminary-Reading-List.aspx

Join the conversation

  1. Do you have an example of a great poll / study?
  2. What is your favourite marketing measure?
  3. What research methodology would you recommend?
  4. Other ideas or concerns you have about marketing research, please state it here.

Of course, I will answer you in the comments. Guaranteed.

This blog entry was recently referred to and summarised here:

Gregory, Goth (2017-08-31) ACM News. Enhancing disease modelling. Communications of the ACM.

See also: Analytics and big data: Security, web, diseases, etc.

Big data can help with rescue efforts after natural disasters. The challenge is to use data smartly to gain insight. This then improves the ability of institutions to respond effectively to future health emergencies.

This entry discusses three challenges that will help manage unprecedented health emergencies better:

1. Detecting an outbreak of a deadly disease sometimes beats predicting when and how it might happen (e.g., ebola outbreak versus this winter’s flu spread);
2. Getting regulators to collaborate across borders quickly – instead of possibly chiding each other – requires them to take action; and
3. Guiding interventions effectively necessitates that resources arrive promptly at the right place – logistics.

Ebola already drains weak health systems in West African countries, such as:

  • Liberia, population 4.2 million: 51 doctors; 978 nurses and midwives; 269 pharmacists; AND
  • Sierra Leone, population 6 million: 136 doctors; 1,017 nurses and midwives; 114 pharmacists.

By the way, some claim that the UK’s NHS (National Health System) employs 10 percent or more of Sierra Leone’s trained doctors.

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So how does this relate to KPIs (key performance indicators), big data, measurement and benchmarking? Glad you asked – I explain below.

Ebola Outbreak in West Africa illuminated the significant threat posed by infectious diseases to human lives and society.

West Africa’s ebola outbreak illuminated the significant threat posed by infectious diseases to human lives and society.

Is the answer predicting an outbreak?

Google Flu Trends (GFT), which tries to predict likely flu outbreaks based on how often people use key search terms, has been shown to be inaccurate. Other methods that make use of a much wider range of data sets are enjoying more success.

For example, business consultancy Accenture, big data specialist SAS and the University of North Carolina say they predicted the US 2012-13 flu season three months before the US Centers for Disease Control (CDC) issued its official warning. That is impressive.

Analyzing social media including blog posts and tweets may give an indication of where people worry about a disease. Unfortunately, this method is far from accurate. More often than not, the number of tweets in a region are the result of news coverage (i.e. similar to Google Flu Trends using search data).

We still do not know if such warning signs (i.e. more tweets or Facebook comments about the flu) are accurate reflections of what is actually happening. We do know, however, that posts to services like Flickr is not that helpful for learning more about the people affected by the disaster (e.g., pictures of Hurricane Sandy posted on Flickr or tweets during the LAX airport incident).

As Facebook (see Facebook mood study: Why we should be worried!) has taught us, online users can be significantly influenced. For instance, more positive items in someone’s newsstream results in more positive posts by that user, though the effect is small.

Given the above, it seems more useful to focus on how we can detect an outbreak faster. In turn, we can put the necessary resources in place faster and more intelligently, thereby saving more lives.

Step 1: Detect the outbreak

Besides prediction, one can also use big data to try and detect an outbreak. Healthmap is a website founded in 2006. It crawls news articles, social media, and other online sources for indications of public health threats. For instance, a timeline published by Healthmap in early March, found evidence of an unusual febrile illness in Guinea, before the World Health Organization announced the outbreak.

Without a system like Healthmap, epidemiologists must rely on hospitals, clinicians, or schools to identify and report outbreaks. This means we need more systems and databases that are built and tested to provide us with such data. Researchers can then access this and provide valuable insights about things like natural disasters and health epidemics.

Rivers, Caitlin M. (October 24, 2014). We could’ve stopped ebola if we’d listened to the data. Retrieved October 26, 2014 from http://qz.com/283206/we-couldve-stopped-ebola-if-wed-listened-to-the-data/

Rivers, Caitlin M., Lofgren, E.T., Marathe, M., Eubank, S., Lewis, B.L. Modeling the Impact of Interventions on an Epidemic of Ebola in Sierra Leone and Liberia. PLOS Currents Outbreaks. 2014 Oct 16. Edition 1. doi: 10.1371/currents.outbreaks.fd38dd85078565450b0be3fcd78f5ccf Retrieved October 26, 2014 from http://currents.plos.org/outbreaks/article/obk-14-0043-modeling-the-impact-of-interventions-on-an-epidemic-of-ebola-in-sierra-leone-and-liberia/

Step 2: Regulators must move their buds

Putting various systems such as Healthmap and others in place will give us more time to identify public health threats. In turn, possible epidemics can be fought faster than was the case with ebola. It will also allow rescue services to respond quicker. The unprecedented ebola crisis has exposed failings in the ability of international and local institutions to respond swiftly.

After the 2010 Haiti earthquake, a research team analysed calling data from two million mobile phones on the Digicel Haiti network (see below).

Figure 2. Estimated distribution of persons who were in PaP on the day of the earthquake but outside PaP 19 days after the earthquake. Circles are shown for communes that received at least 500 persons.

Figure 2. Estimated distribution of persons who were in PaP on the day of the earthquake but outside PaP 19 days after the earthquake.
Circles are shown for communes that received at least 500 persons.

This work enabled the United Nations and other humanitarian agencies to understand population movements during relief operations. It also helped improve our understanding of how people moved during the subsequent cholera outbreak. In turn, these agencies were able to allocate resources more efficiently, and they were now empowered to identify areas at increased risk of new outbreaks.

The crux of the matter is, one must get access to these data rather quickly, or preferably in real-time, and regulators must move quickly. One challenge is that researchers are often elsewhere, so regulators need to find a way to give them access. Of course, neither violating local regulation nor mobile phone users’ rights, such as privacy, is an option. This is further discussed below.

The health emergency in Western Africa has revealed weaknesses, particularly how such things are addressed by the UN, non-governmental organisations (NGOs), and in particular, local regulators.

Most helpful is if these agencies and regulators, including the International Telecommunications Union (ITU) develop a template quickly. The template or checklist should address the steps that must be taken next time, so that necessary approvals are more promptly forthcoming, data is made available for analysis sooner.

A template can make it easier for people to follow an accepted path. It also helps those averse to risk make decisions that help saving lives. This helps move things along, contrary to what we have experienced with the ebola disaster.

Bengtsson, Linus; Lu, Xin; Thorson, Anna; Garfield, Richard; von Schreeb, Johan (August 30, 2011). Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLoS Med 8(8): e1001083. doi: 10.1371/journal.pmed.1001083 Retrieved October 25, 2014 from http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1001083

No author (October 25, 2014). Ebola and big data. Waiting on hold. The Economist, p. 73-74. Retrieved October 27, 2014 from http://www.economist.com/comment/2547851#comment-2547851

Step 3: Guide interventions fast

As previously suggested, regulators need to work with a template for giving researchers access to data, such as that collected by mobile networks, which helps access important data quickly. In turn, insights gained from these data can be passed on faster to guide interventions accordingly. However, we also need better ways to coordinate the fight internationally, meaning that researchers and rescue staff need to use big data records to coordinate efforts.

For instance, the level of activity at each mobile phone mast also gives a kind of heatmap of where people are. It also reveals where and how far they are moving from the epicenter of a quake, for instance.

Telecom operators use call-data-records (CDRS) to manage their networks and bill their clients. The records include:

– caller identity,
– call timestamp,
– phone tower location, and
– number dialed.

Of course, as long as the mobile phone is turned on, phone operators can identify where the phone is, even though the user may not be on the line. The reason is that mobile phones, if turned on, constantly send out signals, which are picked up by the closest tower. This information is needed to allow the person to receive a phone call (e.g., think roaming abroad).

Figure 5. (Top) Number of individuals that visited the university campus during the second alert period (in blue) and its baseline (in red) aggregated daily. (Bottom) The same data aggregated hourly.

Figure 5. (Top) Number of individuals that visited the university campus during the second alert period (in blue) and its baseline (in red) aggregated daily.
(Bottom) The same data aggregated hourly.

Frías-Martínez, Vanessa, Rubio, Alberto, Frias-Martinez, Enrique (not dated). Measuring the impact of epidemic alerts on human mobility. Retrieved October 26, 2014 from http://www.vanessafriasmartinez.org/uploads/epidemics.pdf

Tracking human mobility in disaster areas is vital. It tells us where resources are most needed to help victims. It also reveals how a disease may spread.

For instance, in the study by Buckee et al (see below for reference), accumulating evidence reveals a strong link between human mobility and the spread of epidemics.

Big Data: Applying human mobility data to understand malaria transmission.

Big Data: Applying human mobility data to understand malaria transmission

In the case above, the intention was to alert medics to go where infected people might carry the disease. As Buckee et al have shown, it worked very well!

In addition to trying to point health teams to where they are most needed, the cellphone trackers sent health advice to Haitians via text or voicemail. Examples were things like frequent hand-washing or oral rehydration for those who got sick. Mothers were advised about continuing to breastfeed infected babies.

While these data are updated every single second CDRS are structured. For instance, people who call an emergency number can now be tracked. Such data is helpful and allows researchers to gain insights about how a disease or epidemic spreads.

We must improve detection by using big data smartly. With the help of smoothed procedures (see templates / checklists), regulatory hurdles against sharing data can be removed. Using these data can then guide inverventions on the ground, saving many more lives.

Buckee, Caroline O.;  Wesolowski, Amy; Eagle, Nathan; Hansen, Elsa; Snow, Robert, W. (Jan-Feb, 2013). Mobile phones and malaria: modeling human and parasite travel you can find. Travel Med Infect Dis. 2013 Jan-Feb; 11(1): 15–22. Retrieved October 25, 2014 from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3697114/ doi: 10.1016/j.tmaid.2012.12.003

What is your opinion?

– Have you recently found / experienced a case in which big data helped disaster relief efforts?
– What other recommendations would you make?

I love to read your comments below and look forward to answering them. Merci.

Source: Data analytics: Lessons learned from Ebola


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More resources

Aranka Anema, Sheryl Kluberg, Kumanan Wilson, Robert Hogg, Kamran Khan, Simon Hay, Andrew J Tatem and John Brownstein (November, 2014). Digital Surveillance for Enhanced Detection of Outbreaks. The Lancet Infectious Diseases, Volume 14, Issue 11, Pages 1035 – 1037, November 2014 doi: 10.1016/S1473-3099(14)70953-3 Retrieved October 24, 2014 from http://www.thelancet.com/journals/laninf/article/PIIS1473-3099(14)70953-3/fulltext

Isaac Bogoch, Maria Creatore, Martin Cetron, John Brownstein, Nicki Pesik, Jennifer Miniota, Theresa Tam, Wei Hu, Adriano Nicolucci, Saad Ahmed, James W Yoon, Isha Berry, Simon Hay, Aranka Anema, Andrew J Tatem, Derek MacFadden, Matthew German and Kamran Khan (October, 2014). Assessment of the Potential for International Dissemination of Ebola Virus through Commercial Air Travel During the 2014 West African Outbreak (The Lancet, Oct, 2014) The Lancet, Early Online Publication, 21 October 2014 doi: 10.1016/S0140-6736(14)61828-6 Retrieved October 26, 2014 from http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(14)61828-6/fulltext

Informatics Resources for Ebola Epidemic Response (Resource page). http://ndssl.vbi.vt.edu/ebola/ebola-approach.html

David Pigott, Nick Golding, Adrian Mylne, Zhi Huang, Andrew Henry, Daniel Weiss, Oliver Brady, Moritz Kraemer, David Smith, Catherine Moyes, Samir Bhatt, Peter Gething, Peter Horby, Isaac Bogoch, John Brownstein, Sumiko Mekaru, Andrew Tatem, Kamran Khan and Simon Hay. (September 2014). Mapping the Zoonotic Niche of Ebola Virus Disease in Africa (eLife). Retrieved October 22, 2014 from http://elifesciences.org/content/early/2014/09/05/eLife.04395

Talbot, David (August 22, 2014). Cell-phone data might help predict ebola’s spread. Mobility data from an African mobile-phone carrier could help researchers recommend where to focus health-care efforts. MIT Technology Review. Retrieved October 26, 2014 from http://www.technologyreview.com/news/530296/cell-phone-data-might-help-predict-ebolas-spread/

The Guardian – Ebola funding tracker – interactive

Amy Wesolowski, Caroline Buckee, Linus Bengtsson, Xin Lu, Andy Tatem (September 2014). Containing the Ebola Outbreak – The Potential and Challenge of Mobile Data (PloS Current Outbreaks, Sep, 2014). Retrieved October 29, 2014 from http://currents.plos.org/outbreaks/article/containing-the-ebola-outbreak-the-potential-and-challenge-of-mobile-network-data/


[youtube]https://www.youtube.com/watch?v=az8Ky4utD44[/youtube]

KEY INSIGHTS
Why little data mean a lot: Incremental innovation is key.
Google Trends shows a spike in searches – iPhone6: Remember the flu trends? Increased searches do not make something a fact…
Constant experimentation and rapid implementation: Strive for lots of small and frequent advances, because that is good enough.

We address three questions

1. What does it mean when Google Trends shows a spike in searches?
2. Should we aim for lots of small wins from ‘big data’ that add up to something big?
3. Do metrics that focus on small but useful improvements make sense?

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CLICK - Caution - things may not be as they appear. Check the methods.

1. ‘iPhone slow’ and Google Trends

There are three types of business analytics:

Descriptive analytics that look at historical data,
Predictive Analytics that try to determine what might happen, and
Prescriptive Analytics that focus on giving us different options, in which case we choose what we think suits us best, given time and money constraints.

The question remains whether we have the right data… To illustrate this challenge, we can look at the Google Flu Trends (GFT). Using search results from Google, the GFT supposedly indicates how the flu spreads and affects people in various countries.

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