An ever-increasing global population, a market based on supply and demand, and a culture based on profit have strained our relationship with the environment.
Today, we no longer just take what’s needed, but more than can be replenished sustainably.
The consequences of our actions are reaching an irreversible point, and it’s only now that most governments are realising the need for sustainability. It was this shift in thinking that has helped us channel our ingenuity, innovation and problem-solving skills more meaningfully to harmonise our existence with Earth.
The fishing industry has played a key role in providing billions of people with a livelihood and food on the table; but as with most things, these excesses have led to the near-depletion of fish and shellfish populations across the globe.
The silver lining is that our technological advancements have brought forth an unlikely hero to help us turn the tide: Data science.
Illegal, unreported and unregulated (IUU) fishing is the biggest threat to a sustainable fishing industry, contributing to 20% of the industry’s output.
Regardless of whether there are the necessary legislation and regulations in place, it has been near impossible to monitor fishing activities on a larger scale.
With emerging technologies like machine learning, however, it is now possible to monitor the data gathered through GPS systems and satellites and identify trends and patterns that can help authorities combat IUU fishing.
These databases also have the potential to help lawmakers and governments identify fishing grounds that need protection during certain times of the year. It provides the hard data necessary to implement effective laws and regulations.
In an ideal scenario with the analytics tools now available, in the coming years, the world may see the development of a sustainable fishing industry and an improvement in fisheries management to satisfy the needs of the present without compromising the resources of future generations.
Large commercial fishing vessels and their methods of fishing cause the most damage to the frail ecosystems of the ocean. Their longlines and sprawling nets pay no consideration to the size of their catch, whether it is mature or has spawned.
This diminishes breeding populations, making it near impossible for species like bluefin tuna, for instance, to replenish their numbers to what they were a few decades back.
These vessels are required to send out their location by GPS, however, and this data is invaluable.
This is collected in a large open database and made available to identify illegal behaviour. This wealth of data has supported the development of cloud platform blockchain services and digital platforms that let seafood companies track their supply chain and provide consumers with scannable QR codes so they know exactly if the seafood they are purchasing was sustainably sourced.
If sellers and consumers make their purchasing decisions based on sustainability and ethical practices, we can consider the first battle won.
While there are innumerable benefits to using analytics to combat illegal and unethical fishing practices, there are certain challenges we need to address for greater success in this industry.
The groundwork and the tools necessary to drive the fishing industry towards sustainability are already in place.
While certain roadblocks need to be addressed, introducing forward-thinking policies and implementing data science modelling techniques can help us create a far more sustainable fishing industry than the one we’re struggling to regulate at present.
We always talk about how data analytics platforms can generate the necessary insights organisations need to optimise business operations. But, we seldom dive into the modeling techniques data analysts use to breakdown data and generate useful insights.There are several modeling techniques at an analyst’s disposal, but in the interest of time, we are only going to cover the most essential data science modeling techniques, along with some crucial tips to optimise data analysis.
There are several data science modeling techniques data analysts use, some of which include:
Linear regression is a data science modeling technique that predicts a target variable. It completes this function by finding the “best” relationship between the independent and dependent variable. The resultant graph should ideally ensure that the sum of all the distances between the shape and the actual observation is small. The smaller the distance between the mentioned points, the smaller the chances of an error occuring.
Linear regression is further divided into the subtypes: simple linear regression and multiple linear regression. The former predicts the dependent variable using a single independent variable. Meanwhile, the latter uses the best linear relationship by using several independent variables to predict the dependent variable.
Non-linear models are a form of regression analysis using observational data modeled by a function. It is a nonlinear combination of model parameters and depends on one or more independent variables. Data analysts often use different options when handling non-linear models. Techniques like step function, piecewise function, spline, and generalised additive model are all crucial techniques in data analysis.
Supported vector machines (SVM) are data science modeling techniques that classify data. It is a constrained optimisation problem with a maximum margin found. However, this variable depends on the restrictions that classify data.
Supported vector machines find a hyperplane in an N-dimensional space that classifies data points. Any number of planes could separate data points, however, the key is to find the hyperplane that has the maximum distance between the points.
You may have heard of this term in the context of machine learning and AI, but what does pattern recognition mean? Pattern recognition is a process where technology matches incoming data with the information stored in the database.
The objective of this data science modeling technique is the discovery of patterns within the data. Pattern recognition is different from machine learning because the former is a subcategory of the latter.
Pattern recognition often takes part in two stages. The first is the explorative part, where the algorithms look for patterns without a specific criteria. Meanwhile, the descriptive part is where the algorithms categorise the discovered patterns. Pattern recognition can analyse any type of data, including texts, sound, and sentiment.
Resampling methods refer to data science modeling techniques that consist of taking a data sample and drawing repeated samples from it. Resampling generates unique sampling distribution results, which could be valuable in analysis. The process uses experiential methods to generate a unique sampling distribution. As a result of this technique, it generates unbiased samples of all the possible results of the data studied.
Bootstrapping is a data science modeling technique that helps in different scenarios, like validating a predictive model performance. The method works by sampling a replacement from the original data with certain data points that are not used as test cases. By contrast, there is another method called cross validation, which is a technique used to validate model performance. It works by splitting the training data into different parts.
Most of the data science modeling techniques are crucial for data analysis. However, along with these data analysis models, there can be several viable techniques used to optimise the data science modeling process.
For example, data visualisation technology can go a long way in optimising the process. Staring at rows and columns of alphanumeric entries makes it difficult to conduct any meaningful analysis. Data visualisation can make the process much easier by converting all alphanumeric characters into graphs and charts.
The right data analytics platform can also play a huge role in optimal data analysis. With optimised data analytics platforms, it can increase the rate of data analysis, delivering insights at an even faster rate.
This is where Selerity can help! We have a team of SAS experts that can provide administration, installation, and hosting services to help you optimise your data collection and analysis.
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Technology is developing at a breakneck pace, and analytics is no exception. While SAS business solutions are still the leading analytics platform for most major organisations, other languages have started to make significant inroads into the market, as well. However, there is no denying that SAS is still the preferred choice for most organisations. In our latest blog post, we explore why SAS software is still one of the leading platforms in the analytics industry, despite fierce competition.
SAS, previously known as the Statistical Analysis System, is a platform developed by the SAS Institute for data management, advanced analytics, business intelligence and predictive analytics.
Using a combination of cloud computing, artificial intelligence, and machine learning, SAS data analytics solutions can collect and analyse vast amounts of data to provide actionable results which can be leveraged to get better insights into business operations and refine strategies to accomplish business goals.
First, it is important to keep in mind that SAS software is more than just a programming language. It is a data analysis framework that comes with a GUI. When installed, there is a lot of functionality on offer, like report writing, data retrieval, and operations research.
This provides incredible value for organisations that are looking for cost-effective ways to complete several data collection and analysis functions. SAS delivers incredible value to organisations by creating a more efficient data analysis process.
Despite the growth in popularity of other programming languages, SAS remains prominent because it is still a highly sought after platform. Several prominent business surveys have reported that SAS business solutions are still highly valued amongst organistions, especially those operating in healthcare, finance, and public service.
Furthermore, research also shows that several data analytics professionals above a certain tenure prefer SAS over other progamming languages.
Given the growing volume of data, organisations are looking for platforms that can process it efficiently. This is where SAS platforms offer tremendous value over other programming languages.
SAS business solutions are designed to handle large volumes of data. Considering that most organisations are generating big data, a platform specifically optimised to process it would reap huge benefits for the organisation.
Organisations are looking to incorporate the latest technology into their operations. Technology, like cloud computing and IoT devices, are playing more prominent roles in business operations as organisations look for ways to reduce expenses. SAS business solutions are perfectly suited for this because it works seamlessly with cloud and IoT devices.
SAS is offering several cloud-based products, like SAS Cloud, allowing organisations to cut operation costs and generate insights at a faster rate. Furthermore, SAS and Microsoft have also partnered to bring more cloud-based SAS industry solutions to its customers.
While open-source languages have their advantages, in terms of accessibility, it does not mean that SAS software is a slouch in that department either. In addition to its GUI, SAS applications provide PROC SQL, making it more accessible to anyone familiar with SQL. Furthermore, there are several certifications and training courses on offer to help potential analysts become more familiar with the language.
One of the main benefits of open-source languages is that it is easy to expand its capabilities. However, SAS receives regular updates as well. Even though it’s not open-source, SAS is updated to expand functionality, making it easier to keep up with industry and client demands.
Furthermore, SAS products are tailoured to address industry specific problems. For example, SAS has developed a solution to aid the fight against the spread of COVID-19. The model helps optimise operations vital for curtailing the spread of the virus, like critical response and medical resource management.
Despite fierce competition, SAS remains one of the leading firms for data analytics software. However, there is no denying that there are some issues SAS can address. For example, one of the biggest complaints is the cost of investing in SAS. While some third-party solutions make SAS analytics more accessible, SAS would do well to assess the appeal of open-source technology and see how they can incorporate some of those benefits into their product offering.
The data analytics industry is constantly evolving. This inevitably means new alternatives entering the industry. However, SAS has been an industry leader for over thirty years because of its ability to adapt to meet changing expectations. While there is no denying that SAS business solutions need to evolve, they remain relevant in the data science and analytics industry.
Everyday, 2.5 quintillion bytes of data are generated. With so much information at our disposal, it is becoming increasingly important for organisations and enterprises to access and analyse relevant data to predict outcomes and improve services.
However, arbitrarily organising data into random structures and relationships is not enough. In order to access the data properly and extract the most out of it, it is essential to model your data correctly.
The Big Data revolution has arguably provided a more powerful information foundation than any previous digital advancement. We can now measure and manage large volumes of information with remarkable precision. This evolutionary step allows organisations to target and provide more finely-tuned solutions and use data in areas historically reserved for the “gut and intuition” decision-making process.
Data science modelling techniques play a crucial role in the growth of any organisation that understands the importance of data-driven decisions for their success. Having your data in the right format ensures that you can get answers to your business questions easily and quickly.
In simple terms, data modelling is nothing but a process through which data is stored structurally in a specific format. Data modelling is important because it enables organisations to make data-driven decisions and meet varied business goals.
Typically, a data model can be thought of as a flowchart that illustrates the relationship between data. It enables stakeholders to identify errors and make changes before any programming code has been written. Alternatively, they can be introduced as part of reverse engineering efforts to extract other data models from existing systems.
Data modelling represents the data properly in a model. It rules out any chances of data redundancy and omission, helping analysis and processing. Furthermore, data modelling improves data quality and enables concerned stakeholders to make data-driven decisions. This clear representation makes it easier to analyse data properly. It provides a quick overview of the data, which can then be used by the developers in different applications.
Since a lot of business processes depend on successful data modelling, it is necessary to adopt the right modelling techniques to get the best results.
There are three types of data modelling techniques for business intelligence: Conceptual, logical, and physical.
Conceptual data modelling examines business operations to create a model with the most important parts (such as describing a store’s order system). Essentially, this data model defines what data the system will contain.
Logical data modelling examines business functions (like manufacturing and shipping) intending to create a model describing how each operation works within the whole company. It also defines how a system should be implemented: By mapping out technical rules and data structures.
Physical data modelling examines how the database will actually be implemented, intending to model how the databases, applications, and features will interact with each other. Here, the actual database is created while the schema structure is developed, refined, and tested. Data models generated should support key business operations.
Clearness: How easy it is to understand the data model just by looking at it.
Flexibility/scalability: The ability of the model to evolve without making a significant impact on code.
Performance: You can attribute performance benefits based on how you model the data.
Productivity: An organisation’s model needs to be easy to work with.
Traceability: The ability to manoeuvre through historical data.
In the end, it is all about data: Data comes flooding in from everywhere, data is processed following business rules, and finally, data is presented to the user (or external applications) in a convenient way.
With new possibilities to easily access and analyse their data to improve performance, data modelling is morphing too. More than arbitrarily organising data structures and relationships, data modelling must connect with end-user requirements and questions, as well as offer guidance to help ensure the right data is being used in the right way for the right results.
Business performance, in terms of profitability, productivity, efficiency and customer satisfaction can benefit from data modelling that helps users quickly get answers to their business questions.
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Tax evasion is a huge cost to the Australian government. In 2018, ABC revealed that evasion and fudgers cost the Federal government over $8.7 billion in a single year, prompting the question: Is there a way to tackle fraud to cut losses? However, it is not just tax evasion that is causing the problem. Tax collection practices are laden with problems that make it difficult for corporations and individuals alike to follow the tax code. Fortunately, there is a solution in the form of data science and big data analytics.
Improve tax collection practices
Tax collection refers to the methods authorities use to complete different transactions, like collecting information. Here are a few ways data science and big data analytics streamlines these processes.
Faster data collection and procession
The tax code is a complex beast – one that requires a lot of data to process. However, while businesses and individuals have a hard time delivering the required information, state organisations have a hard time collecting and processing the large volume of information coming in. It significantly slows down the rate taxes are processed and dues are distributed. The slow data collection and processing time is a red tape issue, one of the largest problems state organisations have. However, data science and big data analytics can speed up data collection and processing significantly, which leads to a more efficient tax collection process.
Begin sharing information across different departments
Data science and big data analytics breaks down information silos and encourages data sharing across different organisations. Taxes are often overseen by different departments. For example, the state and national governments have their own codes to follow and little information is shared between the two segments. However, data analytics encourages data sharing because analytics benefits from a large pool of data. Sharing data leads to several benefits for state organisations like faster processing, less waste and a better chance of exposing fraudulent activities.
Data science and big data analytics prevent tax fraud
Data science and big data analytics are the perfect solutions to preventing tax fraud. Here are a few reasons why.
You can differentiate between a legitimate taxpayer and fraudster
One of the biggest problems state organisations face is distinguishing between well-meaning taxpayers and those who try to game the system to either underpay their taxes or exaggerate their income to get a larger rebate. Data science and big data analytics address this problem using data classification, clustering and trail-based pattern recognition to organise taxpayer data based on certain attributes making it easier to separate and distinguish between fraudsters and genuine payers. Data analytics can even be used to track activities in real-time.
Use different sources for analysis
Tax collection entails different variables ranging from income level to job status. Data science and big data analytics are excellent in leveraging both structured and unstructured data. The use of so many different variables leads to comprehensive analysis that allows state organisations to get in-depth insight, and gain a deeper understanding of the situation.
For example, incorporating future GDP projections allows the state to anticipate how much tax revenue they should earn in a time period. The state can then compare projections to what they actually earned to determine how much is lost from tax fraud. Other data sources to inform analysis include deadlines for application forms, declaring business losses, changed residences and so much more.
They scale down information
Tax fraud occurs because there is so much information to process and state organisations have a hard time processing this information in a timely manner, allowing fraudsters to take advantage of loopholes for their own benefit. However, with data science and big data analytics state organisations can scale down information by fusing social relationships. Using analytics, a tax fraud system can reduce the number of suspects and the doubtful transactions associated with them to make fraud detection easier than before.
Reduce fraud with analytics
Banks and many financial institutions are using sophisticated data analytics programs to detect and catch fraud in real-time. Hence, it makes sense for state organisations to take similar measures to reduce the incidents of tax fraud. Tax fraud prevention is complex because both corporations and individuals use loopholes to reduce the amount they pay in taxes or increase the amount they get back in refunds.
However, data science and big data analytics remove these complexities – making it easier to prevent fraud and protect the tax system.
Data science and analytics is changing the food industry for the better. Whether this includes securing supplies to a city or ensuring food quality meets standards, the food industry has a lot of responsibilities. Food is especially important for Australia, where over 65% of the produce is exported abroad. So, it makes sense to look at the technology that allows the industry to do its work efficiently and in less time.
What data science and analytics can do
Food has a shelf life, which causes it to change or expire over time. For example, wine gets stronger over time but fresh produce will expire. Managing food and drink with different shelf lives is a huge challenge for the industry because there are different procedures for each category. For example, the procedure for wine is very different compared to the procedure for dealing with expired produce. But by using data science and analytics, data engineers can predict the shelf life of produce giving the insight needed to take preemptive action to reduce the amount of produce wasted and, in the process, saving money and time.
Social media and review websites have allowed the food and beverage industry to do something that has proven very difficult to do in the past: sentiment analysis. Using NLP, organisations can analyse what people are putting up on social media to discover the patterns and trends that reveal the most popular foods and beverages of the season. It allows brands, restaurants and other organisations to know about the latest recipes that are popular, and adapt accordingly. The insight will help organisations be more responsive to consumer demand.
Better supply chain transparency
Consumers want the food industry to be more transparent. The leading firms of the multi-billion dollar beef industry realised this when they gathered for Beef Australia 2018, a convention that sees over 90,000 visitors. Consumers expect organisations to be more forthcoming with how the food was produced, how the livestock was treated and what chemicals were used in the food – these are just some questions citizens want to know.
Data science and analytics help build transparency within supply chains, so they can be more honest with their customers. Transparency also helps in solving problems and increasing efficiency in supply and logistics. For example, it will be easier to track contaminated food supplies to its storage location, reducing the chances of food-borne diseases.
Measuring critical quality attributes
The food and beverage industry measures the quality of its products using key attributes. These attributes can be a great asset in marketing – for example, the alcohol concentration in beer. However, conventional methods of measuring key attributes are time-consuming.
Sticking with the example of beer, the alcohol level is measured using a method called near-infrared spectroscopy. However, this method is time-consuming and holds up the production process. Data science and analytics allows organisations to explore other measurement methods that are faster and more cost-effective, like the Orthogonal Partial Least Squares (OPLS) which uses multiple regression models to measure alcohol content and colour.
Better health management
Data science and analytics allows organisations to protect food health and cross-contamination. Geographical data combined with satellite data and remote sensing technique allows data analysts to discover changes. This information combined with data on temperature, soil property and proximity to urban areas can predict which part of the farm will be infected with pathogens and take action before the produce is infected. Another excellent example is food inspectors when cities are short on them – data analytics can analyse historical data on 13 key variables to help pinpoint the riskiest establishments, making better use of limited food inspectors.
Data science and analytics to the rescue
Data analytics brings a positive development to different industries, including food and beverage which is great because the industry will face a lot of problems. With the global population growing every year, climate change and desertification of land, the industry will have many problems to overcome.
If they wish to devise unique solutions in an efficient, timely manner, they will need technology that can collect and interpret data in a meaningful way. Data science and analytics allows organisations to collect and analyse data to identify interesting patterns and trends. The technology can also be used to devise several creative solutions to problems plaguing the industry while bringing positive developments to food and beverage.
Data science can provide valuable insights into various aspects of an organisation and SAS is one of the leading data analytics platforms used in the field of data science. With our Selerity analytics desktops, you will have a SAS pro analytics environment to leverage your data science analytics. Give us a call today for more details.
As of 2019, data scientists are one of the most desirable jobs on the market. Organisations desperately need skilled personnel who can comb through data for valuable findings. There’s a severe shortage in the market for skilled data scientists, and as the law of economics states, when demand exceeds supply, the price increases. But that doesn’t mean it will stay that way forever.
Technology is constantly evolving; machine learning and AI are advancing and taking on more complex tasks. Meanwhile, technology once hidden behind a thick barrier to entry, is now becoming exposed as analytics, and machine learning becomes more accessible. All these factors will change the role of data scientists and what they will do in the future.
Trends that will change the role of data scientists
Since there is a huge demand for data scientists, some organisations are turning to automation to ease the burden. The decision makes sense – even if organisations can afford the high salaries, many organisations can’t get the number of qualified scientists they need for the job.
As of right now, automation is primarily used for more routine, tedious tasks. For example, a data scientist spends most of their time cleaning and organising data for analysis. But with assistance from AI, scientists can move on to more advanced tasks, allowing them to be more productive and add more value. Perhaps, they will generate even more valuable findings because they spend more time on advanced techniques like model experimentation.
One reason why there is so much demand for data scientists is because of their variety in skillset. A highly sought after data scientist is skilled in machine learning, coding, data preparation, statistics, databases, data visualisation and communication.
On the other hand, it leads to a stressful work environment. New research reveals that most data scientists suffer from some sort of work-related stress. One of the main reasons identified is that they are expected to balance the technical, business and communication aspects of their role.
In the future, a data scientist’s responsibilities will become more specific as they begin to specialise. The next few years will see new roles spring from the data scientist spectrum, such as data science leaders, data translators and perhaps domain-specialist data scientists (though the last one is highly debated).
Keep in mind that we are still in the early years of big data. As organisations collect more data, the industry will start to mature and grow. Under such circumstances, it’s impossible for one person to fulfil all the needs of a company, which means responsibilities will be broken down into more specialised roles.
When websites were first introduced, it was impossible to develop one without the help of an experienced programmer. Then, WordPress was created, allowing people to create their own websites without the need for developers. Now, we see a similar trend play out with data analytics.
Several new technologies allow professionals to use analytics, AI and machine learning, even if they don’t have a background in programming. Self-service analytics allows professionals to use analytics platforms without a data specialist present. Low code or no code software development programs use graphical interfaces to make coding more accessible, while pre-trained AI models allow smaller companies to gain the advantages of artificial intelligence without a specialist. However, it goes without saying that the platforms that generate the most value and bang for buck will always be the entities that have existed and thrived over the years – most notably, platforms like SAS.
These changes do not mean that data scientists are going to be irrelevant. After all, professional web developers are still needed, despite the availability of a plethora of drag-and-drop web design and CMS tools. However, the democratisation of data and machine learning tools is going to affect their role. Perhaps, data specialists will act as consultants or will only be called upon only when high-end tasks need to be performed. At this early juncture, it’s hard to predict how their roles will change.
Data scientists are in high demand today because organisations need skilled personnel to draw valuable insights from their data. However, the future is going to change the current status quo, automation, the need for specialisation and the democratisation of data will affect what a data scientist does, and how they do it. It’s important to be aware of these trends because our relationship with data will change, and so will the role of a data scientist.
That’s why organisations that provide specialist consultation data analytics services are increasing in popularity. Instead of firms having to spend exorbitantly on expensive in-house staff, they can now access the expertise of an entire team of consultants.
What other developments do you think will change the responsibilities of data scientists?
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Big data has become an integral part of the business world. However, as organisations become more reliant on data, it becomes important to distinguish the tools responsible for cleaning and analysing it. Data science and data analytics tend to be used interchangeably, but there is a difference between the two terms. Understanding the difference is crucial if we are to understand the value they bring to organisations. I am going to address the difference between the two terms, and why it matters.
What is data science?
It’s important to define data science before explaining how it is different from analytics. Data science is a multidisciplinary field consisting of predictive analytics, statistics, machine learning and computer science. The objective is to churn through raw, unstructured data to discover new avenues of study, and find connections between seemingly remote data patterns. The main focus is on finding answers to what we don’t know.
What is the difference between data science and data analytics?
Scope and scale
As you can imagine, the first point of difference between data science vs data analytics is the scope and scale. Data science is much broader, incorporating different elements like machine learning and even analytics tools. The objectives of data science are also much broader in comparison. Data analytics is focused on finding answers to a hypothesis, while data science is about connections and answers without any particular question or hypothesis in mind. Put simply, data science is an umbrella term, while analytics is more focused.
The purpose behind data exploration
The second point of difference between data analytics and data science is the purpose behind exploration. Data science tries to find the connections between data without a question or hypothesis in mind, for the objective is to find potential questions that can be answered in more detail. By contrast, data analytics analyses data with the intent of answering a specific hypothesis. Thus, data science is broader, while analytics is more focused on its exploration of data.
Relevant in different fields
Finally, both data analytics and data science play a major role in different fields. Data science is important in AI, corporate analytics and search engine engineering. Meanwhile, data analytics is vital in industries with immediate data needs like healthcare, travel and business.
Why should the difference matter?
Objectives and targets
Understanding the difference between data science and data analytics is important for organisations. Data analytics and data science use different techniques and will deliver different results, therefore, the techniques should depend on the status of the data set and company objectives. It’s also important to note that data science is used in many cutting edge technologies like AI and machine learning. If companies want to make further advances in AI and machine learning, then more focus is needed in data science. However, this does not mean that data analytics is unimportant. Industries who need to make immediate use of their data to get actionable insights should invest in a suitable data analytics platform.
Mastery in data science and data analytics requires different skillsets. Data analysts need to have knowledge in mathematical statistics, understand data wrangling, PIG, HIVE and familiarity with R and Python. Data scientists require a strong knowledge of R, Scala, SAS and Python, SQL databases, machine learning and multiple analytical functions. Distinguishing between data analytics and data science means you will need to hire the right people with the appropriate skillsets.
This is especially important because the duties of data analysts and data scientists are very different. A data analyst sifts through data, draws reports and visualises these findings to make sense of specific queries. Meanwhile, data scientists spend a lot of time collecting and cleaning data by finding patterns models and connections, testing hypotheses and conducting experiments.
While it’s easy to mix the two terms, data science and data analytics are very different terms. It’s important for organisations to understand this difference. Data analytics looks to answer specific queries, while data science is concerned with finding connections and patterns. The difference between data science and analytics requires differing skillsets and knowledge, which is important to bear in mind when hiring a professional, developing technologies, gaining actionable insights or achieving certain objectives. The main point of difference between analytics and science is that the latter is specific, while the former is broad.
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Did you know that data analytics has played a crucial role in the fight against the Ebola virus? Data science has allowed NGOs and humanitarian activists to curtail the spread of the virus, anticipate outbreaks and much more. Beyond preventing the spread of a deadly virus, NGOs have to tackle many challenges.
Some of the challenges of a non-profit include, but are not limited to, coordinating activities in a crisis, fundraising, as well as managing their funds to make the most out of their money. However, with the rise of data analytics or data science, it is now possible for NGOs to work better and operate faster than ever before.
Here are four ways data science benefits NGOs
A significant challenge for many NGOs is managing their budgets. For most NGOs, budgeting means recording all activities on different spreadsheets. The process might be acceptable for much smaller NGOs, but for larger organisations, it becomes unruly and impractical. It is impossible to properly budget and forecast expenses if the information is in separate spreadsheets.
NGOs can use data science to integrate different spreadsheets and churn out comprehensive reports to see larger trends that they otherwise might have missed. However, NGOs can take it a step further. With predictive analytics algorithms, NGOs can forecast expenses for a project to streamline financial management or improve fundraising activities.
Fundraising is one of the most important activities of an NGO. If they invest too little resources, they cannot fund their projects. On the other hand, if they invest too much, the high costs will cut into their revenue and any gains made will be underwhelming. NGOs need to find the right balance – data analytics is the perfect tool to help find that balance.
Data science allows NGOs to work smarter and create more targeted outreach efforts. Predictive analytics algorithms allow NGOs to identify the people most likely to donate to their cause. Thus, they can create more personalised outreach efforts that will be more effective in garnering donations. Personalised marketing efforts are more effective than generalised outreach efforts because they are likely to encourage more donations. Thus, with targeted marketing, NGOs can streamline fundraising efforts, cut costs and improve donation rates.
When disaster strikes, like the Ebola virus outbreak or the earthquake in Nepal, NGOs have to be at the frontlines to effectively manage the crisis. Managing a crisis is challenging, but what if there was a way to monitor and coordinate activities in real time? NGOs can use real-time information to improve management and coordination across the board. Data analytics platforms take data from different sources to churn out real-time information. Data science and analytics algorithms provide the real-time insight an NGO needs. Furthermore, analytics can generate visual reports for improved coordination of personnel and resources.
When the Ebola virus outbreak happened, a mobile carrier in Senegal provided anonymous access to cellphone data. Analytics took this information and reports from the World Healthcare Organisation (WHO) to track the movements of those who might be infected but didn’t realise it. The information allowed NGOs to effectively focus their activities from curtailing the spread of the disease. Data analytics is invaluable because it draws from different sources of information, ranging from social media posts to cellphone towers. We discuss this point in detail in another post.
Any organisation, be it for-profit or non-profit, needs to manage their operations efficiently to minimise costs. One way to manage operations efficiently is to streamline fund management. The reporting capability of data science provides NGOs with deep insight into their operations, allowing them to cut costs, without sacrificing the breadth and depth of their operations. As an example, India-based NGO, the Akshaya Patra Foundation, reduced the cost of its mid-day school meal program by using data analytics to find the best delivery routes.
The International Data Corporation (IDC) predicts that the world will generate over 163 zettabytes by 2025. The growth of data is a treasure trove for NGOs because the information can be used to improve efficiency, budgeting, fundraising efforts and more. However, the only way for NGOs to take advantage of all this information is to invest in powerful data analytics capabilities. NGOs lacking the resources to integrate data science into their operations have several cost-effective options at hand, like hiring data scientists on a temporary basis.
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