By Mariola Tajs
Companies across the world are producing and collecting the data in exponentially rising sizes. All of the unstructured and structured information, usually dispersed within the organization, don’t provide much value. Data analytics integrates the raw data and transforms it into valuable and actionable insights.
What is Data Analytics?
The modern world revolves around data. Every day, each of us faces the unstoppable growth of the information – we are both producing it as well as consuming. We use the facts and figures in our daily life to improve our decisions – starting from basic questions about what to buy, where to buy and how to buy cheaper, through more complex problems, i.e. where to invest our money. The possibilities are endless and we all agree that the best decisions are the ones that are thoughtful and based on factual evidence. Naturally, the great optimization potential sizes up in parallel with the amount of data – if it can support the decision making process of a single person, the companies can benefit even more by connecting the dots and understanding that they hold a vast amount of profits in their numbers.
The raw data each company produces does not provide any obvious insights. Sometimes overlooked, the stakeholders might not be aware of the fact that joining together many sources can actually guide them and reveal the opportunity. The data analytics process comes in to support them. This term holds different processes and approaches that allow us to identify, transform and analyze the available information in an automated way. Data analytics derives many techniques from the data science field – i.e. machine learning, data mining, statistics – but it is worth mentioning that those two terms are not the same, even though connected on many levels. While data science opens up many data-related possibilities and reveals the data patterns, only with the data analytics we can make impactful and data-driven decisions.
Uses of Data Analytics
Depending on the industry we operate in, or the business unit within the organization, the data analytics can be used in different cases. We can divide them into 4 main types:
- Development process of the products and services
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- Using the data from available sources – i.e. sales numbers or CRMs, we can forecast which products and services are worth investing into, so they can drive the growth and generate additional revenues. We can quickly react and predict the customer needs much faster than the competition and answer to the market demand.
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- Customers insights
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- Information about the behavior of the clients can be highly valuable data. It has the potential to improve the client’s lifetime value as well as answer the questions regarding their buying patterns. Using the different digital touch points with customers (phone apps, online stores, websites and social media) we can make data-driven decisions.
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- Operational analytics and security
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- Data analytics can guide the executives through their journey to optimize the processes and reduce the cost with the synchronous process improvements. Additionally, analyzing the data in depth allows us to spot suspicious activity and prepare to recover from the possible damage.
Data Analytics in business
The outcome of data analytics can be conceptualized for business decisions on different levels of management within your organization. Connecting and comparing different information sources provides a broad perspective on the current state not only of your spending and revenue growth, but also on the business units, allowing you to measure the operational state of your firm.
As the market is evolving very fast, your decision making process needs to keep up with this rapid pace. To ensure that your business is protected and you’re taking actionable steps towards higher profits and smarter choices, build your process on fact-related, valuable insights that the data analytics can deliver.
Data Analytics applications
Potential of data analytics is broad and it enables different industries to improve their operating models. From manufacturing to transportation, every company can utilize something they already have in place- the data they produce. Below we are sharing a few examples showing the potential of the data analytics:
- Finding out the best shipping routes and best delivery time in logistics
- Avoiding the traffics and route congestions in transportation
- Optimizing the operating capacity and deciding on the product development in manufacturing
- Identifying security breaches and malicious behavior in banking
- Increasing the sales and understanding the customer’s demand in retail
Data Analytics process
The data analytics process depends on many factors, including the types of analytics, size of the data, company focus, and, last but not least, the question we want to answer. Although the possibilities are endless, there are a few steps that we need to follow in order to go through the process:
- Collect the data requirements: understand the business case and determine what kind of data you are going to use.
- Identify the data sources and how to access them: gather the needed data and organize it.
- Clean the data: check any major gaps, make sure about the right structure, remove the data points that are not useful, major errors and duplicates.
- Analyze the data: depending on the problem you try to solve, find the best data analytics method and use the most appropriate tools.
- Visualize the data and interpret it: convey your insights to address the business case, go beyond sharing only raw results – make use of the right visualizing tool and look at the data from different perspectives.
Data Analytics tools
Before deciding on the tool you would like to utilize for your data analytics work, you need to recognize the business needs for a specific case. Take a look at your organization and check what kind of resources you already have, and what are the capabilities of your co-workers. Before choosing the analytics or reporting tool be mindful about the budget and assess whether the tool can easily integrate with the company systems. Choose based on the level of technicality of your users – make sure that when used by non-technical people the tool should be intuitive and easy to learn. Prepare for a potential scale up and possible modifications and think about it when choosing the pricing program. With this in mind, you are ready to explore the market, choosing from a variety of available tools. The most popular ones are:
- Power BI, Tableau, Qlik Sense (reporting tools)
- Oracle Analytics, SAP Analytics Cloud, Google Analytics (analytics tools)
- Microsoft Excel, Google Data Studio, Apache Spark, Python, R (data processing tools)
Data Analytics methods
In order to approach the data analysis within the firm, we can choose from the 4 different types of it: descriptive, diagnostic, predictive and prescriptive. Starting from the most basic one – descriptive analytics – we are able to understand the previous patterns in our data. If we want to understand why certain trends or events happened, we use the diagnostic type. When the anomaly arises, i.e. we notice that we paid 20% more for a specific raw material in January compared to the previous months, we can reveal the reason for this anomaly by using available tools and connecting different data sources. Taking a step further, with the predictive analytics we can assess, with the high level of certainty, the impactful trends that can help us make better actionable decisions. If we, as a manufacturer, want to understand which production line is worth investing our money in, we can make use of the probability algorithms on our historical data. The last type is prescriptive analytics. It can show us the potential benefits of different predictive analysis outcomes which allows the business users to choose the most profitable way to achieve their goals.
For the purpose of performing data analytics, one has to have the knowledge of available methods that can enrich the process. Below, you will find a list of the 5 essential techniques that can respond to your business needs:
- Cluster Analysis
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- Finding similarities within the given dataset, by grouping and classifying it into clusters. It allows to unveil the hidden patterns in the data as well as the differences between specific categories.
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- Regression Analysis
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- Using the historical data for the purpose of discovering the relation between the dependent and independent variables. Depending on the outcome, we can predict the values (i.e. product price), given a specific dataset. It’s a useful technique that allows you to analyze the trends and forecast the possible outcomes.
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- Time Series Analysis
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- Analyzing the data collected in regular or irregular intervals (days, weeks, months, trends, variations) to understand their development over the time and determine whether the variables depend on each other.
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- Neural Networks
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- Advanced technique that mimics the way neurons in the human brain develop insights. The more data we provide, the better the outcomes are, as neural networks advance and develop by learning from every data transaction.
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- Decision Trees
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- Tree- shaped diagram that displays the flow of the decision making process using the branches that present the potential risks and benefits. The information provided consists not only in qualitative information, but also quantitative data like revenue estimation. It allows us to compare different paths and choose the one that is the most profitable for your organization.
Data Analytics role
Data analytics can be a powerful and advantageous tool, if used in the right way. Using the data you produce as a firm can support you in overcoming different limitations and address a wide range of challenges in the process of optimizing your business. The 4 most impactful areas where you can leverage data analytics are:
- Decision making process: when you combine big data with data analytics you can achieve a competitive advantage and you can easily avoid choosing the paths that result in poor decisions decreasing the profitability and poor operational excellence.
- Mitigating risk: applying machine learning models on real-time transactions can identify fraud and help you protect and enhance security within an organization.
- Operational improvement: from better supplier management and inventory management to production delays – usage of data analytics can support you in re-thinking the current organizational plan and improve it, so all the resources are utilized efficiently and effectively.
- Customer behavior: in depth market analysis allows you to react much faster to changing demand and improve the customer experience. You can keep your customers loyal to your brand with better understanding of the current and future trends as well as recommending them services and products based on their preferences.
What is the role of data analytics in healthcare?
The healthcare system is producing a massive amount of data – to name a few – data from different sensors or devices that monitor various health parameters, data originated straight from the patients or information systems implemented at medical facilities, data from laboratories and health practitioners.
Implementation of data analytics in healthcare can bring a long list of needed improvements:
- Predictive modelling can support forecasting treatment outcomes and support preventing potential risks associated with the illness development.
- Data analytics can support health practitioners in the process of diagnostic and identifying the disease causes.
- On a broader level, from the societal perspective, healthcare big data can be used to prevent spreading diseases and to address the current health related issues in patients by monitoring disease trends and outbreaks.
- Available data can definitely support medical research and can play a big role in the process of inventing new ways of treatments or diagnostics.
- As healthcare systems around the world are very expensive and difficult to manage, data analytics can reveal the savings potential and cost reduction with sustained patient-centered medical care.
Role of data analytics in marketing
Owning an interesting brand that has a constant buyer’s attention is not an easy task in the modern world. Customer preferences are changing constantly and the market is answering to these changes really fast. When creating a marketing strategy it is crucial to look into the data and make sure that the ads are personalized and accurately targeted to the client requirements.
The past purchases data and real-time transactions can support the organizations to engage in the new segments or markets, optimize customer relationship management and improve the forecasting of clients’ preferences. These actions can drive further improvements, such as:
- Higher return on investment – the revenue growth is solely connected with marketing activities. Analyzing the ROI can guide the executives and teams how to associate marketing activities with rising profits.
- More accurate predictions about customer lifetime value by utilizing predictive data analytics. Applying models and algorithms on the past purchases and different factors that impacted them can support forecasting the customer engagement.
- Better understanding of the market campaign costs associated with creation of a lead, especially cost-per-lead. How much the organization spends on lead creation and how much revenue it brought? These questions can be definitely answered with a data analytics toolset.
- Improved lead-to-customer conversion rate by identifying the leads generating the most profits and building the knowledge based on successful campaigns. This can also help the teams in choosing the right path to avoid redundant spending or incorrect investments.
Role of data analytics in pharmaceutical industry
Data analytics can have a broad potential in the pharmaceutical industry as these organizations can benefit in many different fields – from accelerating the process of drug development and discovery to improved clinical trials and safety measures. Even the recent months and the COVID-19 pandemic showed how important it is to utilize the data and speed up the vaccination development process. So where can the pharmaceutical industry leverage its data-driven insights? Let’s dive into the 3 main areas:
- Discovery and development of drugs: gathering and utilizing the knowledge created by scientific groups in the form of the research papers or sourcing the data from clinical trials is definitely a time consuming activity. Implementing predictive data analytics can accelerate and support the teams to drive further innovations in this field.
- Clinical trials efficiency: data analytics can support the teams in the process of identifying the patients, based on a set of needed factors as well as monitoring trial outcomes: better care and identification of the potential side effects.
- Cost reduction: in order to balance the profitability and increasing prices, the companies need to make sure that they increase their revenue with no harm for patient safety. With data analytics they can look into a set of crucial KPIs such as: rebate as a percentage of total drug spending, drug utilization review savings per member per year and improve their decision making process to maintain the efficiency growth.
Final thoughts
Data analytics is the critical tool that helps the companies grow on different levels, including revenue growth and operational excellence. Modern organizations understand that applying the right models on the raw data can enhance the decision making process and maximize profit.
Looking beyond the standard data approach the organizations can associate valuable insights with actionable steps and take better care of their customers, improve the product and services development, as well as enhance productivity. Data analytics is here already and it is important to use it when enabling the business to undergo a digital transformation.
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