Are you talking about data analytics like every other discerning business owner or company? If you’re not, reconsider your school of thought. Perhaps you need to reawaken to the fact that data analytics is no longer the province of multinationals and big-box companies.
Let’s imagine that you know how analytics is transforming business processes and decision-making. What all can agree is that having superior data analytics skills can position your business competitively. It’s the norm for modest businesses seeking growth successes in a disrupted world.
If your excuse to drift away from data analytics is your start-up status, smile. Small businesses are leveraging data analytics and its benefits to scale to levels they’ve only envisioned on paper. Since data is the new business fuel, it’s only wise to take some time to learn, internalise, and employ what data and analytics have in store for budding organisations and businesses.
Today, data science analytics is an essential business tool. Advanced computing and the Internet of things (IoT) now enable businesses to leverage various metrics. There are many facets to understanding data analytics. Where can you start?
What is Data?
A simple definition of data is raw information. Business and consumer activities online and offline amount to business data. A specific definition points to data being demographic info, behaviour, trends, personal attributes, and business-to-customer interactions. Some big corporations gather data on the most unassuming activities online and leverage it to optimise delivery.
Data can be structured or unstructured
- Structured data is highly defined in a way that makes it easily searchable. It’s stored in relational databases. Examples of structured data include addresses, names, dates, credit card details or stocks, and geolocation information.
- Unstructured data has no pre-defined format. It’s stored in its native format and it’s only processed when the need to use it arises. Examples include videos, email, webpages, social media posts, presentations, chats, IoT data, photos, and audio files, among others. There’s no sin in using quantitative and qualitative data to describe the above. But, before we digress, let’s go back to data analytics.
What is Data Mining?
You, like many other business owners, keep asking, “How can I derive real business value from the massive data clusters?” The solution lies in data mining. What does it entail? Data mining is the technical process of sifting through, sorting, classifying, and organising datasets. Think of it as organising a huge store such that your customers and agents can point out a specific product instantly.
Data mining leverages techniques, such as clustering and classification. It requires the use of proper applications or tools with data filtering, clustering, and cleansing features.
These tools work on the data to help organisations get a better view of customers. You can leverage these tools to reinforce your strategies and make better use of your resources.
What is Data Analytics?
Data analytics is the calculated use of technology, processes, and people to extract unique insights from preferred data sets. The findings are then used to modify, enhance, or improve operations depending on the type of business, its niche, and its prevailing circumstances.
Data analytics, as a science, analyses raw information using automated techniques, processes, and algorithms. This is what makes mountains of data easy to consume. The techniques used reveal metrics or trends that may end up swallowed in the mass of data in evolving databases.
Data analytics is the swivel that is making more businesses wheel towards a data-driven approach in their core operations. Analytics can help you identify problems before they take over your business. It’s the data that helps you map out solutions for issues even before they occur.
Types of Data Analytics
Distinct businesses engage in different stages of data processing. They depend on the phase of the workflow and the analysis requirements. There are four analytic types:
This is a basic form of analytics. The initial stage consists of resizing big data into fathomable sizes. Descriptive analytics seeks to identify the findings and underlying processes.
Diagnostic analytics seeks to identify why a particular aspect or phenomenon happened. This stage makes use of data mining, discovery, and correlations. Diagnostic analytics seeks to understand what happened. It determines the factors and triggers that lead to a particular scenario through probability and chance.
Predictive analytics, as the name suggests, entails predicting possible future outcomes. Predictive analytics cannot accurately predict future occurrences. Instead, it estimates the chances that a given event may occur. It creates models that use current data to deduce a path that future data is likely to follow.
Prescriptive analytics surpasses the other analytic facets. It helps to chart a preferred way forward riding on the other three. This model enables visualisation of outcomes if specific steps are taken. Also, it recommends individual courses of action that can lead to the desired outcome. It leans on a feedback system to anticipate the connection between an action and its outcome.
Characteristics of Data Analysis
Characteristics of the data analysis process depend on various aspects, including velocity, volume, and variety. These characteristics make it different from the traditional analysis process.
Instead of using manual data exploration options, it’s possible to employ codes to programmatically explore the same big data due to its sheer scale.
Data analytics experts may turn to hypothesis methods to analyse data. However, you can skip the hypothesis and drive the data with a machine-based formula. It works for big data.
With superior computing capacity, data chunks can be iterated over and over until you get the desired outcome. With traditional analysis, you’d need a huge memory to make desired modes work. If you have Infrastructure as a Service capacity, you enjoy more machine power to process and analyse more data quickly.
The Data Analysis Process
You gradually understand the anatomy of data and analytics. Relax, you still need to know the steps that companies and start-ups use to analyse it. It’s what the analysis process entails.
1. Define a need
Nothing trumps the need for data analysis. It’s about what is happening. You can define the need based on aspects such as dwindling sales, soaring business, customer satisfaction, or cart abandonment reasons. You need a data management plan that ensures your data in and out tray is organised and accountable.
2. Collect data
Collecting data isn’t difficult. You can harness structured data from internal sources. This can be your CRM, OR, and ERP systems, or market automation applications. You have a breadth of open-source tools that can help you collect external data patterns.
3. Exclude duplicates and inconsistencies
After you collect your data, the chances are that there are redundancies and repetitive, inconsistent data. You need to get rid of those since they could misinform the process.
4. Analyse data
Data analysis can take different forms. You can use your business intelligence tools to create reports, charts, or graphs. These make it easy to decipher and understand your data sets. Satisfied? If not, consider data mining for in-depth analysis.
5. Take action
Congratulations, the last thing you need to do is to put your findings into use. The analysis needs to show you a particular course of action(s). If it doesn’t, consider reviewing your analysis processes to see if you might have missed critical pointers.
How Business Analytics Can Help Your Business
Are your customers switching allegiances due to poor user experiences? Do you know the right steps your business needs to take in the analytics process? Unfortunately, you have no reason to not leverage your experiences given the host of benefits it offers. Implementing data analytics to drive intelligent and effective business decision-making has its benefits. But it’s not A-B-C easy. What you cannot ignore is that your Singaporean business needs it more than ever. Here’s what data analytics can do for your business:
Identify business opportunities
Analysing critical data can reveal new opportunities that you might have ignored. You can identify untapped customer segments. Using an intelligence-based approach opens you up to endless opportunities that your competitors don’t know.
Data analytics does more than streamline your operations and drive the return on investment (ROI). Analytics gives you a sneak preview of your audience. This makes it easy to target ads that resonate with them.
Find your audience
Data analytics help you listen to what people say about your brand. Analytics provides you with social media chatter. If you combine that with customer behaviour, it’s easy to identify the ideal audience that you should be targeting. You can do it better than everyone else and keep customers coming in.
Data helps you understand your target audience better. Data analytics arms you with creative insights that help your venture better market to your customers. You can preview your campaigns and polish them for optimal results.
With the right analytics tool(s), you can determine the demographics that will identify with your marketing campaign. It becomes easy to modify your targeting strategies and messaging. It results in more conversions and less effort.
Better customer service
Data analytics literally gives you customer insights on a silver platter. This simplifies how you personalise your offerings to fulfill consumer needs. Even if it doesn’t lead to stronger customer relationships, it breeds brand loyalty.
Improved decision making
Insights from data analytics can help companies revitalise their decision-making processes. You no longer rely on smart guesswork to make critical marketing, content product development, or planning decisions. Think of analytics as giving you a 360-degree view of your customers. Isn’t that adrenalin enough to drive your growth projections?
Delivering relevant products
Effective data gathering and analytics help you to keep abreast with the ever-changing business landscape. You’ll be in the know as soon as new trends or technology hit the market. It enables you to innovate and develop new products that your customers are yearning for.
Developing marketing campaigns
Haven’t your datasets told you what your competitors are up to? Then what are you waiting for? The insights you glean enable you to craft and send the right messages to the audience you want to draw to your new business. Data analytics will help you to create new offerings and enhance brand recall through creative marketing campaigns.
You deserve a pat on the back if you’re about to expand. If you have tons of consumer, procurement trends, equipment supply, and delivery, don’t sit on it. You can exploit such data to inform you and assist you in planning for the expansion.
Creating your business plan
If data can tell you how you’re performing, it can show you how you’re likely to perform in the next phase you’re planning to launch. Insights from data help businesses to forecast sales, trends, and turnover. Such information is crucial if you want a winning business plan for the next venture.
Mitigating risk and fraud
Security and fraud analytics are part of the bigger analytics picture. These are what keep your business safe from internal and external fraud incidences. Done the right way, analytics can help unearth inconsistencies that encourage fraud.
Steps for Successful Data Analytics
There’s no need to overemphasize how data analytics grants you a competitive edge. You already know that. The idea is not to hoard all the data you can get. It is about deriving value that can drive your ROI and profitability. YOU need to know the steps that lead to success with data analytics.
1. Define business use cases and outcomes
Always align data analytics with business-centric growth plans. Your strategies should work with practical suggestions that are proven through progressive measuring and tracking of crucial metrics.
2. Appoint data experts from your business and IT departments
Selecting experts from different departments in your organisation contributes to the success of your data efforts. Make sure they are individuals with the right aptitude and knack for progressing the analytics cause.
3. Establish proper infrastructure, tools, and architecture
The right analytics tools, data architecture, and experts assist your business in wading through the data project successfully. However, you’ll need to find the right tools that your personnel like to use.
4. Get the right data/analytics resources
You have the right analytics team and your project is set to launch. However, make sure that they have the resources they need if they require additional support.
5. Manage the project in phases using measurable indicators
You can’t take all of data analytics in with one massive bite. Start small in manageable and measurable segments. Always prioritise a specific area. You will realise that this approach helps to reinforce data usability and quality.
6. Involve data integrity protocols
It pays to assess your data proactively to discern critical elements and areas that require adjustments. It’s the only way you’ll glean insights that enhance decision-making. You need to establish data quality controls moving forward. Consider making this a companywide responsibility.
Data Analytics Technology
Data analytics isn’t another new fad. But the volumes of new data require advanced tech if you expect valuable insights. Today, some technologies make big data analytics useful to organisations. They include:
Machine learning and Artificial Intelligence
Machine Learning (ML) and Artificial Intelligence (AI) are always intertwined. They power algorithms that can decipher data while mimicking human intelligence. Machine learning aids in digesting and deciphering data to give predictions. There’s no human contribution involved in the process. MI-based algorithms can be programmed to deduce small data clusters and advances as more data is added.
Data cannot be analysed if there are no data flow and organisation procedures in place. You must maintain high quality for the data you collect and you need a data management platform for that.
The process of categorising, clustering, and classifying data to get valuable insights is indispensable. It’s the only way you can navigate data sets to glean relevant information. Today’s data mining technologies enable companies to execute analytics quickly.
Predictive analytics is the technology that enables you to assess cumulative data to foretell future occurrences and possible outcomes. The predictive analysis employs machine-based logic and algorithms to provide accurate results and insights.
Examples of Data Analytics Tools in the Singapore Marketplace
- Azure HDInsight
- Tableau Public
- Splice Machine
- Apache Spark
What Is Big Data and Its Impact on Businesses In 2021 And Beyond
We have defined and explored data mining techniques. And the various steps of conducting successful analytics. Such a determining business use cases, establishing infrastructure, getting the right analytics tools, and implementing integrity protocols. Now, let’s dive in and explore its impact on business in 2021 and beyond.
Leading industries that utilize big data solutions include banking and financial services, resource mining, digital marketing, e-commerce, and logistics and transport. Businesses are optimizing their operations and managing their data traffic by investing in big data solutions. Vendors are adopting it to improve their supply chain management.
Banking And Finance
Advanced technology such as machine learning, AI, advanced analytics, big data, and the cloud has transformed how financial companies handle their processes and compete in the market. Big companies utilize these technologies to meet consumer needs, implement a digital transformation, bolster profits and loss.
Utilizing data-driven optimization has enabled financial institutions to explore the full capabilities of unstructured and high-volume data in discovering new market opportunities and gaining a competitive advantage.
The value of data obtained and stored assists organizations in creating predictive analysis, uncovering patterns and establishing predictions. All this is based on how well the data is gathered, processed, stored, and obtained. For this reason, these institutions are increasingly adopting cloud data solutions.
Cloud-based solutions are flexible, scalable, save on costs, and integrated across all business applications to help analyse diverse data sets to gain valuable insights to make better-informed company decisions.
Here is how big data has impacted the banking and financial sector.
Real-Time Stock Market Insights
With machine learning, companies can analyse stock prices, political and social trends affecting the stock market. Monitoring trends in real-time allows the analysts to evaluate and gather valuable data and make intelligent decisions.
Fraud Detection and Prevention
Big data through machine learning can analyse and interpret consumers buying patterns and hence detect security risks. Banks can freeze credit cards and transactions and notify customers of the security threats when they notice any security concerns with their credit cards.
Accurate Risk Analysis
With big data, financial institutions can make calculated decisions based on predictive analytics that considers customer segmentation, economic status, and business capital to identify risks like bad debts and band investments.
Some big data benefits in finance include:
- Increased customer satisfaction and revenue.
- Automated manual process that saves time and offers insights based on customers’ daily transactions.
- Cloud strategies have improved customers’ paths to purchase, giving room for gathering daily metrics, predicting performances, and improvising data analysis.
- It has streamlined the workflow and reliability of the core banking data and application systems through uniform integration platforms.
- Data integration has enhanced the automation of daily reporting improving productivity and allowing businesses to access and analyse critical insights.
Resource Mining Industry
The mining industry is using big data to reduce the cost of mining operations and improve efficiency. To fully utilize data obtained from business and predictive analytics, mining companies have identified ways to collect, manage and prioritize data to build interfaces that will improve daily operations.
Mining companies monitor mining operations and activities in real-time therefore helping them gain a competitive advantage. Large pools of data are gathered and analysed to obtain patterns and gain insights to enhance productivity and creating value through waste reduction and boosting the quality of services and products.
Here are some strategies to maximize big data in the mining industry.
Unlock Data to Improve Company Operations
Mining companies obtain vast amounts of data daily—extensive data analysis can help to get insights that can predict market demands and optimize supply.
Improve Efficiency by Reducing Downtime
Big data assists in equipment management. By predicting any downtime, operators can implement repairs and hence ensure efficiency by reducing downtime.
People Management and Excellent Operations
With big data, managers can obtain accurate information on performance ranging from product inventories to sick leave days, revealing the variability and boosting performance.
Comprehensive data analysis can significantly improve decision-making processes, identify and minimize risks, and unearth valuable insights.
Besides controlling operations, Mine site managers can comprehensively understand the most site productive days and figure out the underlying factors. They can then use these factors to increase productivity and efficiency in the future.
Big Data on E-Commerce
According to research published by BARC, there are four main ways in which e-commerce benefits from big data:
Understand Customers Better
For an e-commerce business to grow, it must adopt big data technologies to gather and analyse customer behaviour, needs, and experiences.
Data analysis can help identify and predict customer needs and expectations to increase customer satisfaction and retention hence business growth.
Big data can also inform your marketing strategy through analysis of search results trends. A good analysis of search results can help you curate better SEO marketing efforts by understanding which ones are popular amongst your customers.
Make More Strategic Business Decisions
50% of structured data obtained from the Internet of Things (IoT) is used in decision-making. E-commerce can use big data and real-time analytics to determine which customers have the highest long-term value so that they can invest and spend more money acquiring, targeting, and retaining those customers. Hence making a more strategic budgeting decision.
Improved Operational Processes
Operational processes can benefit from analysing customer behaviours and their shopping data. By implementing predictive analysis, businesses can determine the average checkout time and use this data to improve customer experience.
Similarly, big data algorithms can help e-commerce analyse market trends and supply chains which helps identify maximum inventory levels warehouses need to maintain to operate effectively.
Amazon, for example, utilizes big data to improve its operational processes. It monitors customer behaviour, shipping details, and personal information and uses this data to link with manufacturers and track inventory, ensuring that all orders are shipped on time.
Big data allows e-commerce corporations to cut operational costs by investing in third-party logistics and leveraging economies of scale, which reduces costs per team and optimizes the overall working methods.
Big Data and Digital Marketing
Digital advertising is continually evolving, and with big data, digital marketers can analyse massive structures and unstructured data to identify and discover new trends and patterns and gain actionable insights to make informed decisions.
As a result, marketers can implement data-driven marketing by curating more personalized and highly targeted online and mobile ads. Other impacts include;
By using big data analytics platforms, companies can capture, store and analyse both structured and unstructured data from sources such as photos, videos, and social media posts to gain relevant insights that inform marketing decisions and strategies.
Here are three examples of how big data can impact digital marketing success.
Designing Effective Marketing Campaigns
Companies can curate valuable and compelling content that targets and addresses their customers’ core needs by collecting customers’ behaviours. One example is the use of cookie files that collect data on customers’ activities on the browsers generating personalized data in the process.
Making Better Pricing Decisions
Traditionally, businesses priced their products and services based on competitor pricing, product cost, and perceived value based on customer demand. However, with big data, many factors such as customer preferences and general economic information can affect pricing decisions.
For instance, businesses ought to consider whether their customer’s disposable income, what products they have purchased over the last five years, whether they afford to pay for the products and services. Incorporating all these insights can lead to making better pricing decisions.
Showing Appropriate Web Content
Digital marketers use the knowledge base to curate customized, more engaging content for their audiences. Netflix is a perfect example. It provides individualized recommendations of movies based on what they have watched. By checking the time, your visitors spend on your sites can provide insights into what they are interested in, and you use this information to curate relevant content based on their browsing history.
Logistics And Transportation
The logistics and transportation process has become complex due to increased traffic, fuel processes, and government regulations. However, with big data, the process has been simplified. Here is how;
- Utilizing Big Data in The Transportation of Goods
Big data can help companies track the delivery of goods and supplies. With the help of this technology, the entire transportation process can be monitored from start to finish in real-time. The recipient can also track their purchases on their smart mobile gadgets, thus making the whole process transparent, benefitting both the customer and the business.
You can use big data to determine and assign the shortest routes along with sensors installed in vehicles. It can also help you obtain data related to traffic updates and weather reports. Thus, assisting logistics service providers in enhancing supply chain processes and building robust models for collaboration between sectors.
Big data technology monitors and tracks stock supplies and provides insights in real-time. Therefore, improving promptness, performance, and efficiency in “smart” warehouses logistics by maintaining stock in their stores.
Improving customer service
Implementing big data technology in social media and surveys can help obtain customer feedback and improve customer service. Companies can improve their delivery time, meet customers’ expectations, and strengthen their relationships, boosting business revenue.
Big Data Trends
Machine Learning (ML) To Explore New Arenas
ML and AI will extend their abilities by incorporating more advanced algorithms and intelligent services. These features will integrate the company’s processes to obtain insights and develop faster and robust problem-solving techniques. In addition, they will provide new opportunities for analysing data flow from various sources like Google search, applications, sensors, etc.
Market Research Future (MRFR) forecast that the ML market will grow from $7.3B in 2020 to 30.6B in 2024.
Increased Demand for Natural Language Processing (NLP) Systems
Companies will focus more on techniques that recognize a customer’s voice and translate it into machine language, making it easier for companies to gain insights into customer attitudes and preferences and accomplish better outcomes for their various projects.
NLP will establish ways to build virtual assistance with conversational capabilities, and big data will help the concept’s success in coming years.
Data-as-a-service (DaaS) demand to grow
DaaS provides data in categories customized to customers’ needs. However, few businesses have acquired resources to access and utilize that data efficiently.
With Big data as service companies gain more control over their data and fill gaps in their departments. Utilizing cloud-based solutions makes it easy to access relevant data in a safe and easy-to-use format.
Big Data to transform skills sets
Big data market forecast companies to search for talent based on industry-specific expertise instead of only institutional degrees. Chief Data Officer (COD) position has become a trend in 2021. The specialist is appointed to address challenges related to data availability, integrity, and security
A survey by NewVantage shows 76% of industry-leading companies have a CDO, which is expected to improve in the coming years.
Data transmission using 5G
The ever-growing number of IoT sensors has increased demands for more reliable and robust internet connectivity, increasing 5G deployments. The technology will allow a high-throughput data pipeline that can handle large data volumes from various sources.
Big data will be powered by 5G at the industry level to provide quick, efficient, and safer ways to collect and process data in addition to improving user experience by connecting VR-assistance, wearable tech, and health-monitoring devices.
Rapid Growth in Cloud Data Technologies
Traditional methods involve companies expanding their premises physically to scale operations and analyse data. Big data and cloud computing services enable organizations to store and process vast data regardless of location.
The Wunderman Thompson report project that 2021 may be the turning point in the on-demand play with companies like Microsoft, Sony, and Facebook are predicted to invest more in cloud-streamed gaming services.
Big Data Challenges
1. Data Complexity and Reliability
As more data continues to be generated, companies need to learn how to handle vast volumes of data and develop solutions for processing raw data.
2. Data Security
Data is obtained from various sources and hence challenging to determine which is compromised. Organizations will therefore have to establish appropriate practices for data collection and retrieval.
3. Data Protection
Almost 42 million messages are shared on WhatsApp every 60 seconds, and this number is expected to rise, according to VisualCapitalist. There will be a consistent change in data regulation and protection with the increased amount of sensitive data.
Top Data Analytics Trends in Singapore in 2020
Domination of augmented analytics
Other than business intelligence and machine learning, organisations are opening up to augmented analytics. It empowers experts to draw more insights from data clusters. Already, vendors are infusing augmented analytics into their products to improve user experiences.
Rise of IoT, ML, and AI
Singaporean executives can attest to cloud computing being on top of the analytics games in the next few years. A majority of them foresee the impact of IoT, AI, and ML on their analytics activities.
In Singapore, organisations are not fixated on basic analytic strategies and capabilities. The shift is moving towards upskilling their workforce with analytical savvy personnel who can improve decision making based on data
Data as a Service (DaaS)
Who thought that DaaS would become a highly sought-after resource? Today, businesses are investing in DaaS to mitigate the enormous data volumes in different verticals.
Small and established Singaporean businesses struggle to find the nexus between disparate data points. Thanks to graph analytics, most of them can now deduce data in simplified visual formats. Graph analytics is transforming sophisticated data interpretation and is here to stay.
Internet of Things meets data analytics
Singaporean ventures that employ the Internet of Things (IoT) in their analytics processes have been experiencing exceptional results. IoT data analytics are helping businesses to decipher the data coming out of IoT-connected devices.
Tips for Choosing Data Analytics Tools
Annually, there’s a plentiful release of data analytics tools that offer a range of features and functionalities. Data isn’t your average resource to handle manually. But you need to know how to go about procuring data analytics tools.
Check business objectives
You know too well that your IT tools need to sync with the existing infrastructure and data architecture. But you need to define your business objectives before you invest in a given tool. You’ll need an application that gives you easy access to data and reporting features that help meet your objectives.
It is possible that you build and maintain a proprietary data analytics tool. Even so, you need to appraise the costs. If you purchase such a tool, consider maintenance, hidden fees, or subscription costs. Like always, don’t go with the cheapest.
Assess user interface and visualisation
Your workforce will be using the said analytics tool to help augment businesses decisions. Don’t make it a nightmare for them and you. A tool with a user-friendly interface makes it easy for your non-tech staff to execute and decipher dashboard reports. You don’t want the wrong interpretation since you know how it can affect decision-making.
The analytics tool you want to invest in should help identify data patterns, trends, and possibilities. Consider one with features that are beyond simple numerical calculations. It’s better if it supports graph analytics.
Do you want to access your datasets and analyse them remotely? If yes, you can prioritise tools that support mobile analytics. Even if you’ve not gone global yet, you’ll need to analyse data on the go as you scale.
You know your business is unique, and the data you collect is equally so. You must select a tool that meets your specific analytics needs. You might need bespoke analytics set up and you know too well that a generic, one-size-fits-all solution won’t suffice.
What Does a Data Analyst Do?
You’re probably asking yourself what kind of expert handles data analytics. A data analyst does. But what exactly is their mandate?
- Data analysts collect, systematically collate, interpret, and filter complex data to help different organisations and businesses.
- Data scientists employ data analytics techniques and software to make swathes of disparate data useful for businesses facing a decision-making crunch.
If you didn’t know, this is how organisations end up making informed decisions. They gain witty muscles to optimise campaigns, content, strategies and develop products that boost their brand recognition.
Don’t be fooled into thinking that data analytics experts work with a magic wand. On top of the application they leverage, they come packed with superior skill sets. Their duties include:
- Employing computerised models to extract core data meanings
- Refining and deleting shady data
- Performing preliminary analysis to determine data quality
- Running algorithmic analysis to discern data meaning
- Executing end analysis for additional data screening
- Preparing reports based on findings and presenting them to decision-makers
Key skills of a data analyst
- Distinct numerical and mathematical aptitude
- Proficiency in programming languages, such as Python, SQL, or Oracle
- Data modeling, interpretation, and analysis knowledge
- Accuracy and troubleshooting skills
- Methodology, logic, and critical thinking skills
- Attention to detail
- Interpersonal and teamwork skills
- Excellent communication skills
Top 5 Technical Data Analyst Skills You Must Have to Land a Job in 2022
To begin, it’s critical to comprehend what a data analyst works. At the risk of repeating myself, all data analysts are concerned with data. They employ technology tools to sift through massive amounts of raw data and derive significant insights in the process. Additionally, data analysts are frequently tasked with cleaning up damaged data, establishing the data’s quality, and creating reports for their company.
As you might have imagined, each of these jobs requires data analysts to have a well-developed toolkit of technical abilities. Here are a few points to consider.
- Visualisation of Data
As the phrase implies, data visualisation is the capacity of an individual to display data results graphically or via other depictions. The aim of this is straightforward: It enables a more intuitive grasp of data-driven insights, especially for people with no background in data analysis.
Data analysts can use data visualisation to assist corporate decision-makers (who may lack extensive analytical knowledge) in seeing trends and comprehending complex concepts at a glance. This skill enables you — the data analyst — to acquire a complete picture of a firm’s position, communicate actionable insights to team leaders, and even influence company decision-making for the better.
- Data Cleaning
Even basic algorithms can provide astonishing insights when used in conjunction with a properly cleaned dataset. On the other hand, filthy data might generate erroneous patterns and lead a firm to incorrect conclusions. By definition, data analyst credentials necessitate adequate data cleansing abilities – there are no two ways about it.
MATLAB is a multi-paradigm statistical computing environment and programming language that enables the development of algorithms, matrix operations, and data visualisation, among other purposes. Businesses engaged in big data have shifted their focus to MATLAB because it enables analysts to reduce their pre-processing time data significantly and enables rapid data cleaning, organising, and visualisation. Notably, MATLAB can run any machine learning model created in its environment on any platform.
R is one of the most widely used and prevalent programming languages in data analytics. Given its ubiquity and usefulness, any prospective data analyst should place learning R at the top of their priority list.
- Calculus and Linear Algebra
When it comes to data analytics, sophisticated mathematical abilities are a non-negotiable need. Some data analysts even pursue undergraduate degrees in mathematics or statistics to obtain more profound knowledge of the theory that drives real-world analytical practice!
In analytics, two distinct branches of mathematics take centre stage: linear algebra and calculus. Linear algebra is used in machine and deep learning because it can perform vector, matrix, and tensor operations. Calculus is also utilised to construct the objective/cost/loss functions that instruct algorithms on accomplishing their goals.
20 Common Data Analytics Mistake
It should go without saying that the modern marketer needs one critical skill: data mastery. As growth marketers, a significant portion of our job is to collect data, report on it, and crunch the figures to complete analysis. The age of gut instinct in marketing has passed us by. The only path ahead is through astute data analysis and application.
However, in order to become a data master, it is vital to understand which typical errors to avoid. We’re here to assist you; many advertisers make fatal data analysis errors and you don’t have to.
Ignorance of Measurements Other Than Numbers
Certain data analysts and advertisers study solely the statistics they get without contextualising them. Once this is established, quantitative data becomes invalid. In these instances, whoever conducts the data analysis will question “why” rather than “what.” Being swayed by huge numbers is a common blunder made by a large number of analysts.
Inadequately Defining The Issue
This may be viewed as the tone of an essential challenge in data science. The majority of data science challenges occur due to an incorrectly characterised problem for which a solution must be discovered. If you cannot articulate the problem, attempting to solve it adequately is pure fantasy. One will thoroughly investigate the issue and analyse all of its components, including stakeholders, action plans, and so on.
Using The Incorrect Metric
When you’re just getting started, it’s easy to focus on tiny victories. While it is beneficial and a terrific morale booster, be careful that it does not divert your attention away from more critical measures (like revenue, customer happiness, and so on).
Failure To Clean And Normalize Data
Always begin with the assumption that the data you are dealing with is incorrect. Once you become familiar with it, you will start to “feel” when something is not quite right. To begin cleaning up your findings, use pivot tables other quick analytical tools to search for duplicate entries or inconsistent spelling. Additionally, not standardising the data is another issue that might cause the research to be delayed. In most circumstances, normalising data removes the units of measurement, allowing for easier comparison of data from various places.
Inadequate Treatment of Outliers
Because outliers impact any statistical study, analysts should identify, eliminate, and real outliers as necessary. The method used to deal with any outliers should be documented for auditable analysis. Often, the loss of knowledge in return for greater comprehension is a reasonable trade-off.
Many individuals overlook outliers in some circumstances, which substantially influence the study and skew the conclusions. Other times, you may be excessively focused on the outliers. Despite this, you invest a significant amount of time on matters that may be irrelevant to your studies.
Someone should not place excessive confidence in their model’s correctness to the point where they begin overfitting the system to a given case. Analysts develop machine learning models that apply to a variety of circumstances. Overfitting a design may just make it work for an occasion identical to the one in preparation. In this example, the model would fail miserably for any situation other than the training set.
Ignoring Seasonal Data
Holidays, summertime, and other seasons cause your data to be corrupted. A three-month trend is also explicable, owing to the hectic tax season or back-to-school season. Ensure that your account for seasonality in your data, including weekdays and afternoons.
Establishing campaigns without a clear objective will result in insufficient data collection, insufficient findings, and a fragmented, worthless report. Asking a generic query such as “how is my website doing?” will not delve into your data.
Alternatively, you might continue your efforts based on a straightforward test hypothesis. Pose direct questions to yourself to clarify your aims. For instance, inquire, “How many page views did I receive from users in Paris on Sunday?” Provides a straightforward comparable metric.
Bias in Sampling
Sampling bias occurs when you infer from a collection of data that is not representative of the demographic you are attempting to understand.
You might, for example, launch a test campaign on Facebook and Twitter and then assume that your whole audience falls into a given age bracket based on the traffic generated by that test. However, if the identical Snapchat promotion were conducted, the traffic would be younger. In this scenario, the audience’s age range is determined by the medium through which the message is communicated and is not necessarily indicative of the total audience.
Comparing disparate data sets is one strategy for mitigating sampling bias. Therefore, when evaluating two or more sets of data, consider avoiding making unfair comparisons.
Unequal contrast occurs when two data sets with unequal weights are compared. A typical example of this is comparing two reports from two distinct periods. They may be a consistent month over month, but they are likely to be uneven if they ignore seasonality or the weekend effect. For example, the Christmas season is predicted to impact online traffic for an eCommerce site in December. As a result, it cannot be directly compared to March’s traffic figures.
The Data Analyst Must Be Acquainted With The Definition Of A Metric
To be blunt, advertising employs a great deal of language. Even if you’ve been around for a while, metrics might be oddly labeled or defined differently. When you don’t, it’s all too simple to assume you do.
The bounce rate is a perfect illustration of this. When we recently questioned a group of advertisers, they unanimously determined that the bounce rate was due to tourists exiting the site too quickly.
It is just inaccurate — the bounce rate is the percentage of guests who leave a site after seeing only one page. And this is not to say that a high bounce rate is always a bad thing. You may imagine, for instance, that your bounce rate is high on a site with few pages.
This is an easy trap to fall into since it may have a negative impact on a variety of marketing methods. Assume you ran a campaign on Facebook and immediately noticed a significant boost in organic traffic. Of course, you may believe that your Facebook promotion drove traffic to your site.
This conclusion may be incorrect because assuming that another directly produces one behaviour may rapidly get you in hot water. Indeed, there are parallels between the two phenomena. Another effort might also be to blame for the increase in traffic, seasonality, or any of several other circumstances.
Dredging of Data
Assume you have an excellent collection of data and have been effectively testing your hypothesis. While it may be enticing, avoid trying many fresh ideas on the same data set.
While this may appear to be a simple approach to get the most out of your labour, any discoveries you make are likely to be coincidental since you’re ready to perceive connections that aren’t there in your initial output. These are ominous signs of concurrent connections to be noteworthy.
A great strategy to avoid making this error is approaching each data batch with a new, objective hypothesis. And, when the theory evolves, a unique collection of data is used to update the study.
Data Analysts Struggle Due To A Lack Of Statistical Significance
You’ve conducted a check, gathered data, and determined a clear winner. However, be cautious not to jump to conclusions without first establishing some statistical validity.
This is a common occurrence while doing A / B conversion testing, as the results may appear evident at first glance, with one test outperforming the other. However, caution should be exercised in jumping to conclusions prematurely.
To correctly categorise the winning variation, ensure that it has a high probability and statistical significance. For instance, when doing A / B tests to obtain an accurate result, we recommend a 96 percent likelihood and a minimum of 50 conversions per alternative.
The Difference Between A Mobile And A Desktop Computer
Cross-platform marketing has grown crucial as customers migrate to the web in more significant numbers. However, it can cause considerable difficulties.
Mobile and desktop require distinct tactics and, hence, opposed methodological approaches. Make no mistake about combining the data sets into a single pool and evaluating the pool as a whole.
Users behave differently on desktop and mobile computers, and their data should be treated separately to allow for practical analysis. In this manner, you’ll obtain knowledge that you would have been unable to get otherwise, and you’ll receive a more accurate picture of actual customer behaviour.
Outliers should be viewed as one aspect in a study, not as valid indications in and of themselves. This is because online data is complicated, and outliers in the information mining process are unavoidable.
The standard approach is to dismiss an outlier as a fluke or to pay excessive attention to an outlying as a good indicator. As with other things, truth is frequently somewhere in the centre. For instance, blaming an unusual decline in traffic on a seasonal influence may omit a more severe issue.
Why Are These Data Science Errors Occurring?
Having an insufficient understanding of the business or technical competence necessary to tackle the problem is a cause for these typical errors. Appropriate market perspectives, target markets, and technological skills are required before specialists may begin working hands-on.
Reduced time for the final evaluation will expedite the analysts’ work. As a result of their inability to follow a suitable checklist, analysts frequently lose little pieces of information.
Information science is a vast subject, and acquiring a comprehensive understanding of data science is a steep learning curve for any newcomer. The majority of data science blunders occur as a result of the fact that the majority of experts are unaware of several outstanding data science characteristics.
Data mining is a combination of art and science. You must be both calculated and innovative, and your efforts will be rewarded. There is nothing more fulfilling than resolving a data analysis issue after several measures. If you do it right, the rewards to you and your business will be significant in terms of traffic, leads, revenue, and cost savings.