#dataanalytics

Top 6 mistakes in data strategy development process

Any company, no matter the size, needs a data strategy. Only data can provide a business with answers to all questions, thereby improving operations and increasing profits. However, an ineffective strategy makes it impossible for a company to tap its full potential. Here are 6 the most common mistakes in data strategy development process.

  1. Outdated strategy

The world is rapidly transforming. The business strategy must fully comply with the requirements of the 4th industrial revolution. The data strategy, in turn, must support the business strategy and be relevant in the modern world. Investing in an outdated strategy is pointless. This not only does not work, but also leads to inappropriate costs.

  1. Ignore business goals and objectives

Not all existing methods of data analysis are important and effective in achieving the business goal of a particular company. Many companies develop a data processing strategy based on interesting cases. However, they do not take their own interests into account. It is important to define goals and objectives, figure out how the data can be used to achieve them, and only then begin to develop a strategy.

  1. Do not define indicators of success

How can you determine how successful or unsuccessful a strategy is? – determine the KPI. Only by defining the key performance indicators, it is possible to correctly track and analyze the progress. Any data initiative must have a business case with appropriate KPIs.

  1. Concentrate only on technical costs

Implementing a data processing strategy entails technical costs. However, do not forget that changes will be required in the corporate culture. Each team member must rely on data every time he makes a decision. To do this, it is necessary to organize a training process for collecting correct data, assessing their quality and analyzing.

  1. Consider only structured data

At the moment, data comes in different forms – photographs, sound recordings, text files, and much more. The data strategy must take into account structured and unstructured data to get the correct information. Don’t ignore external data sources from repositories, governments and data brokers as it can be useful.

  1. Ignore ethics, confidentiality and legal aspects of data

Any data project should start by defining aspects of the ethical and confidential data usage. Consumers must be confident in the confidentiality of their data usage, and also benefit from it.

The correct data strategy is a key element in the process of achieving business goals. It should be based on 3 to 5 use cases. The use cases for each business will be different, however, they should be consistent with the business strategy.

The main analytics levels in the automation process

Data by itself is not too valuable. What makes it valuable is analytics and insights. Data analytics helps to get the current situation, draw the right conclusions and make decisions that will lead to more efficient business activity and growth.

Let’s consider a car example. Often, we do not think about how it works, but we know that it can go wrong. It is possible to identify the causes and prevent them using analysis. It is enough to collect 2 datasets: the first is about car workflow and the second is about time when the car fails. Such dataset analyzing will help to understand what actions lead to failure and identify ways to prevent recurrence in the future.

Descriptive analytics

Data scientists call the analytics in the example above «descriptive». The result of this analytics type is the description of «what?», «when?» and «why?» happened. However, it does not offer possible solutions.

Now modern companies mostly use descriptive analytics. Specialists get access to the full amount of information in the form of graphs, charts, tables, reports that show the recurrence number of a particular event. This can be interaction with a client, a mechanical failure, sales, etc. Next, the specialist’s task is to analyze all information and develop the action plan.

A good example of descriptive analytics is Google Analytics. This tool perfectly visualizes indicators of site traffic, its load, user behavior on the site and much more. Descriptive analytics can be useful when used strategically.

Predictive analytics

The goal of many companies that adopt artificial intelligence and machine learning technologies is to enable predictive analytics.

An idea if predictive analytics is to analyze data about what has already happened and make predictions. Let’s go back to the car example. There is problems report from January to March. Prediction algorithm provides information on how and when a machine may fail in the future.

The result of predictive analytics is a set of probabilities that do not give 100% confidence that an event will definitely happen in the future. However, having such information, it is possible to prepare, check the availability of spare parts for repairs and develop a «plan B».

Predictive analytics is actively used by banks and lenders to assess risks. In consequence of analytics bank employees receive an estimate of the payment likelihood by a particular applicant and can compare it with risk thresholds.

Until recently, only advanced companies could implement analytical technologies and evaluate benefits. Now these companies are moving to «prescriptive analytics».

Prescriptive analytics

So, predictive analytics provides an answer to the question «what can happen», but does not answer the question «what to do to obtain the optimal result?» The next step in analysis is prescriptive analytics.

The predictive analytics system provides a number of possible outcomes. This can be effective when the specialist exactly knows the effective solution. For example, choose a specific action to increase sales. However, if the goal is to increase revenue in general, it is necessary to know the most effective measures package. This is where prescriptive analytics can help.

Let’s take autonomous vehicles as an example. The car must «know» that the fastest left/right turn can be lengthened by heavy traffic for a certain period. In this case, it is necessary to choose the best option and «register» it to the computer that drives the car.

With prescriptive analytics, the person driving the machine can know not only the cause of the failure, but also the best way to minimize such situations.

The 3 levels of analytics described above are fundamental to the automation process. Until recently, advanced predictive and prescriptive analytics technologies were prohibitively expensive for most companies. Now there are a large number of analytical tools and platforms that small organizations can afford. Gradually progressing through the analytics levels contributes to a successful digital transformation.

Big Data and Business Transformation

During the  time, all new technologies become simpler and more affordable for large-scale use. Now Big Data is going through this phase. As a result, different industries transformation is taking place. Here are some examples of key industries influenced by Big Data.

Retail

In recent years, selling and buying procedures have changed a lot. However, both online and offline store owners use the data to better understand customers, their needs, and comparison with the current offer. This approach ensures an effective operation and allows for huge benefits.

Data analytics is applicable to almost every step of the retail process. By predicting trends, it is possible to determine the demand for a product, optimize the price, determine the target audience, and gain a competitive advantage.

Health care

Big data in healthcare is helping to improve disease detection and treatment, improve life quality and reduce mortality rates. The main Big Data task is to collect as much information as possible about the patient and identify the slightest changes and illness signs at the earliest stages. It prevents disease development, provides a simpler and more affordable treatment protocol.

Financial services, Banking, Insurance

Big Data helps financial companies and banks detect fraudulent transactions. Insurance companies use Big Data to establish fairer and more accurate insurance premiums, improve marketing efforts, and detect fraudulent claims. British insurance company Aviva is offering a discount to drivers for being able to control their driving using smartphone apps and car devices. It allows insurers to observe how safe is driving.

Manufacture

The production process is changing dramatically with the development of robotics and the automation level. Sportswear, footwear and accessories company Adidas is actively investing in automated factories.

In traditional manufacturing, Big Data matters too. With the help of built-in sensors, it is possible to monitor the specific equipment performance, as well as collect and analyze data on its effectiveness.

Education

Now, data is being collected about how people learn. This information is used for new ideas, defining strategies for a more effective learning process, highlighting ineffective areas of the learning process and ways to transform it. In one Wisconsin school district, data was used for almost everything from defining and improving cleanliness to planning school bus routes. The performance data analysis of a particular person in online learning mode leads to the personalized, adapted learning development.

Transport and logistics

There are cameras to monitor inventory levels in warehouses. With the help of data from the cameras it is possible to provide reminds about replenishment. Also, this data using machine learning algorithms can be transmitted to train an intelligent inventory management system. In the near future, warehouses and distribution centers will be almost completely automated and require a minimum of human intervention.

Transport companies collect and analyze data to improve driving behavior, optimize transport routes, and improve vehicle maintenance.

Farming and agriculture

Traditional industries also use data to generate new opportunities. American manufacturer John Deere has applied Big Data techniques and launched several services. They enable farmers to benefit from crowdsourcing real-time data from thousands of users.

Energy

The volatility of international politics complicates discovering and producing oil and gas process. Royal Dutch Shell has developed a «data-driven oilfield» with the aim of reducing the cost of its production.

Hospitality business

Recreational service providers use data to make their customers happier. The main goal is to ensure each room profitability, taking into account seasonal changes in demand, weather conditions, local events that can affect the number of bookings.

Professional services

The professional services like accounting, law and architecture are also changing as a result of advances in data, analytics, machine learning, artificial intelligence and robotics.

For example, accounting software allows to automatically import transactions, track digital receipts and taxes, and automate payroll calculations.

Big Data – big opportunities

Now it is too popular to discuss such term as Big Data. But not everyone clearly understands what it is and which value it has. Let’s figure it out in order.

So, big data is a huge and complex dataset from different sources and constantly growing in volume. 3 main characteristics of big data: high speed of reception, large volumes, variety. Big data is mainly used to solve business problems in consequence of information content depth and width. At the moment, many organizations already work with big data, reaping the full benefits of its usage.

10 main areas where big data is actively and successfully applied:

1. Customer understanding and targeting

At the moment, this ​​business area actively uses big data. The main goal is to understand better customers, their behavior and preferences. Companies gain a more complete picture of their customers by expanding information sets with data from social media, browser logs, text analytics, and sensory data. The main goal is to develop predictive models.

2. Business processes understanding and optimizing

Companies use big data to understand better operational processes and improve its efficiency. For example, companies can optimize their inventory based on forecast data, web search trends, and social media data.

3. Personal indicators assessment and optimization

Big data can be useful not only for organizations, but also for people. For example, a person who owns a smartwatch or bracelet receives certain data every day (number of steps, number of calories consumed per day, activity level, sleep pattern, etc.). The correct this data usage brings benefits to the user.

4. Health care system improving

Big data analytics can decode entire DNA strands in minutes, develop new medicines, and better understand and predict disease patterns. Also, it is possible to track and predict epidemic outbreaks, monitor newborns in specialized departments.

5. Athletic performance improving

Big data analytics are widely used among elite sports. At the moment, it is already developed IBM Slam Tracker for tennis tournaments. Video analytics is actively used, with the help of what it is possible to track individual football or basketball player’s performance during a match. Sensor technology of sports equipment helps to obtain data about the game and improve it. Smart technologies can be used to track the routine of each athlete: his diet and sleeping mode, his emotional state through messages on social media, etc.

The US National Football League (NFL) has developed a platform that allows to make effective decisions by analyzing the pitch condition, weather conditions, statistics of the individual players results.

6. Science and research development

Science and research field is empowered by big data. The European Council for Nuclear Research (CERN) conducts various experiments to reveal the Universe secrets, its origin and existence, generating huge data amounts. Big Data computing power can be applied to any dataset, discovering new opportunities and sources for scientists. Researchers can easier access census and other data to create more accurate picture of public health and social sciences.

7. Machines and devices performance optimizing

Big data analytics enables to create smarter and more autonomous hardware. Big data technologies are used to drive self-driving cars, optimize computers and data warehouses performance.

8. Security improvement

Big data is widely used in this area. Thus, the US National Security Agency (NSA), by analyzing big data, has the ability to prevent terrorist operations. Big data analytics is also used to detect and prevent cyber-attacks, to catch criminals, predict criminal activity, and detect fraudulent transactions.

9. Cities and countries improvement

Big data is used to improve many aspects of life in cities and countries. For example, optimize traffic by analyzing traffic conditions in real time. Big data analytics is also used to transform a city into a «smart» city, where transport infrastructure and utilities processes are combined.

10. Financial markets

Big data is widely used in high-frequency trading. Decision making processes take place using big data algorithms. Share sale is carried out using big data processing algorithms that consider signals from social media, news sites, etc. It allows to buy and sell shares in a matter of seconds.

Data analytics improves website performance

Successful business is based on information. Information quality and timeliness are the key of effective activity. Data analytics gives the business many opportunities: right goals setting and the most accurate result achievement, forecasts creating, trends identifying, strategy development, possible risks identifying and their consequences preventing or minimizing.

Among other things, analytics is also important in determining the company’s website effectiveness. Working with data on site performance and marketing strategy from multiple sources enables management to determine the company’s ability to meet expectations.

Website traffic check and comparative analysis with competitors’ websites provide a clear picture of business development on the Internet. The lack of this information makes it quite difficult to run business.

Let’s consider several reasons why analytics is important in the business management world:

The target audience is rarely stable and predictable, so its analysis is critical for strategy development, plans generation and goals setting. Such analysis may contain information about the age categories of users, their preferences, the platforms they use to browse the website. Tracking online users’ behavior makes it possible to timely identify various changes, new trends and promptly respond to them. Therefore, it optimizes the business marketing strategy and leads to effective operations.

There are many indicators, by determining which it is possible to say exactly how effective the site is. For example, a high bounce rate means «something» on the main page that prevents users from navigating further to other website pages. The reasons can be different: too much information on the main page, long page load time. Also, the reason may lie in incorrect marketing – the user’s request does not match the site information content. With the help of analytics, you can identify all the causes, eliminate them and set up correct and effective marketing.

Uncertainty and unexpectedness always provoke stress and frustration. Nescience and understanding lack of how to act can be a big problem for business owners. Failures can happen with a strategy and possession of all the information. However, understanding the reasons why a particular situation happened minimizes stress level and makes it possible to determine alternative development way. The analytics essence is to provide all the necessary tools to create an effective marketing and optimization strategy.

Analytics is the main tool for modern business management. Analytical tools implementation and usage make it possible to understand the business, track causal relationships and optimize the company’s activities.

Innovations that have transformed Business Intelligence

One of the most common questions that business owners ask themselves is «How to be competitive and outperform competitors?» A couple of years back the main characteristics for getting a competitive position were price, design and advertising. Now they are not the only ones. Understanding the client, his needs, willingness and ability to pay are the main characteristics that determine a competitive advantage. Data and BI are too important here because they can help to reply to these questions. The 3rd BI generation release gave a possibility to afford analytics tools to every business user and bring to the light the full data value. The current business intelligence scenario is driven by innovations.

Let’s consider some of them:

  1. Technology shift

Let’s come back to 2000’s. The appearing of a big number of users promoted the appearing of a big data amount. Such situation led to the purchase of high-performance desktops with CPU servers that had bigger memory and direct attached storage. The first 2 generations had a data-oriented stack as a design point. Unlike its predecessors, the 3rd generation is moving towards a network-oriented stack. The first important technology shifts began to emerge during of major vendors IBM and Oracle consolidation. Early BI solutions often were installed on desktops and it was difficult to deploy enterprise software products globally. Over time the Internet became the main design point. In consequence, web-based architecture was developed, and it offered easy installation process and faster deployment options.

  1. Data about data

Realizing that analytics is an enterprise-wide function and not limited to desktop, vendors strived to develop well-managed and secure products seeing the enterprise involvement. Products were built around a metadata layer. A metadata repository stores and manages metadata.

Metadata types:

  1. Storytelling with data

Earlier BI solutions made use tools focusing on reports and dashboards. However, architecture evolvement promoted the functional development of BI solutions. It will not be difficult for a qualified specialist to make evident insights looking at a specific data set. But it’s impossible to say the same about all business participants. Information only in the form of graphs or tables is not always fully understandable for users. For comprehensive understanding of the data meaning, solutions that allow to use the storytelling technique were developed.

Analytics as a service (AaaS)

For the last 10 years was happened a number of technology advances that have changed the way of companies’ relation both between themselves and with their customers. We were overseeing keynote jump to the modern smartphone, «power shift» in the social media world where Instagram overtopped Facebook, appeared new generate technologies that have fixed firmly in the business. And finally, cloud was appeared. Big Data, AI, ML and cloud gave an impulse to develop a new product – «analytics as a service» (AaaS).

Business is increasing in a rapid rate and together with it data is growing too. For processing of such data volume companies need to make essential investments in different resources (human resources, software and hardware). But AaaS can solve this problem.

Analytics as a service is rated as an analytic software service via Internet. By using this technology solution organizations receive an access to data analysis without development in-house technologies that allows to cut expenses.

The demand for analytics is not limited by one business area but outspreads for all: manufacturing, retail, healthcare etc. Considering individual requirements of each company AaaS-providers and their customers have endless possibilities. This solution could be easily included into resources planning, manufacturing execution system, cloud services and security police.

AaaS benefits for business:

  1. Business insights

The market changes rapidly, consumers’ expectation grows constantly, macroeconomic conditions change dynamically. Considering such terms, it’s not enough for business to go with the times but it has to be one step ahead. And it’s not possible to make without analytics. Independently of scope each company is in need of in analytics. «Analytics as a service» can help here paving the way from data to ideas and solutions. With the help of AaaS company’s management easily get predictive data on the bases of which develops more effective business strategy.

  1. Improved customer experience

Satisfied customer is a formula for business success. That’s why one of the main tasks for each company is to increase customer loyalty by improving customer experience. With the advent of online channels data collecting has increased that stimulated companies to provide multichannel service. AaaS helps to unify data from different sources and create the single source of the right data.

  1. Predictive statistic for everyone

AI analytics is an indispensable business tool. With AaaS organizations get access to it and arrange data-driven decisional process across all hierarchy. It helps to save some time and resources.

  1. Competitive analysis

Companies have an opportunity to collect data from several sources, track field trends and seasonal changes and use this information in business processes. Deep analysis allows to compare company’s rates with business rivals’ rates and develop a targeted strategy for better results achievement. With self-service function AaaS users can rapidly and easily analyze data and create necessary reports.

Market is growing invincible, many new companies are appearing there and a business struggle is also growing accordingly. For flying level companies need to use advanced technologies and run analytics in daily routine.

Big Data Analytics can improve customer experience

Currently the world is composed of data that every day is being produced by businesses as well as individuals. According to a tally, in 2020 each Internet user created 1,7 MB of information every second, Google processed more than 40 000 queries, about 3 million emails were sent by people around the world. And those rates continue to rise rapidly.

This amount of data processing requires advanced analytic solutions, specifically big data analytics.

What is this?

Big Data Analytics (BDA) – is the complex of advanced analytic methods that are focused on the work with big and different size data (from terabytes to zettabytes) to uncover valuable information from structured, semi-structured and unstructured data sets from different sources. By using this tool, it is possible to uncover hidden patterns, unexplored correlations, market trends and customer preferences. BDA comes with technologies and methods like predictive analytics, statistic analysis, text analysis, data visualization, machine learning, artificial neural networks, spatial analysis, Data Mining, pattern recognition, simulation etc.

World data amount stupefies but opens huge capacities for business. For instance, in working with customers and their behavior. By collecting and analyzing data for each client it becomes possible to provide an individualized offer upon request. As a result, a company becomes more competitive, customer experience becomes higher and revenue is growing.

Some examples how BDA can help to improve customer experience:

More analytics – more possibilities

Data and analytics are the main tools of the modern business in the digital world. Trend of additional data and analytic decisions searching is actual currently. Organizations demands are growing, they want to find one technology for satisfying most of their requirements. It was a reason for advanced analytics developing.

Despite visible benefits of modern technologies usage, some companies stay away and don’t understand how they can use them. It’s mistaken opinion that advanced technologies are applicable and useful only for major companies like Google, Microsoft, IBM etc. Another one barrier is investments required to technologies implementation into the enterprise. Advanced analytics is a decision of such questions.

What is advanced analytics?

Advanced analytics is the combination of technologies (machine learning and analytics) for automatization of whole data pipeline (from data processing to results generation). It could be compared with an umbrella that includes many disciplines and has high use. Such kind of analytics is used in all business areas for events forecasting. For example, advanced analytics in marketing is used for understanding customers’ preference and their behavior changes.

Gartner describes advanced analytics as autonomous or semi-autonomous data examination with the help of sophisticated techniques and tools, that promotes deeper understanding, more precise predicting and recommendations creating. It gives a possibility for companies to perform calculation like «what if», that are used to forecast trends, events and behaviors. Advanced analytics is comprised of such areas as artificial intelligence, predictive analytics, data mining, data visualization, semantic and graphic analysis, neural networks etc.

Advanced analytics advantages:

 Applications of advanced analytics in business

  1. Right data gathering

Data is the bases of digital era. In consequence of analyzing and coming up with data-driven answers business processes became easier. It gives an opportunity for management to make different decisions easier and more efficiently. But the main task is to identify and collect the right data. Advanced analytics give a possibility to correctly identify necessary qualities and make them operational for goal achievement.

  1. Creating business-model to optimize results

Business-model creating comes from capabilities definition. Here data mining technology is actual.  This tool allows to make a lot of tests that will help to identify submerged patterns. However, a result will depend on how efficiently executives can use received information. Advanced analytics is a perfect assistant in this task and creating of business-model according to the working system.

Main data trends 2021

After 2020 everyone has feelings that the world will not be the same. It was enough restless and astable year for the whole word. But at the same time, it became an acceleration of digital transformation processes. Business and people had to accustom and fit into a new reality. Less digitized companies have become more vulnerable during the pandemic compared to high-tech market players. However, it also became a motivation for such companies to run digital. They had to digitize their processes, upgrade business-models, provide access to data and advanced training for team. COVID-19 has also become a proof that the data plays a big role, and everyone can use it to inform or misinform.

Data science evolves and matures and consequently many organizations try to increase their digital stability and switch over to the data-driven model. Critical important tasks like self-driven car development, protein folding, and algorithmic trading programs have been conducted using data science methodologies and technologies. It’s just a little part of examples. Data science using is much wider, new and improved data science tools will appear in the coming years.

The main data trends and forecasts for 2021

1. Forecasts with the help of data analytics

One of the main 2021 trends will become real-time analytics. According to forecasts, the number of connected to Internet of Things devices will reach 24,1B by 2030. Organizations collect much more data than previously and try to transform it into analytic information that can help to solve business tasks. Real-time analytics transforming data into insights gives a possibility to respond to situation instantaneously.

2. Databases

For the last 40 years companies have histed their databases locally. However, in 2021 and the coming years there will be a trend of databases deploying or migration to the cloud. According to forecasts, cloud databases will grow up to 75% by 2022. It is a reason of different requirements appearing that most likely will come with developing on cloud-native databases, more closely incorporating analytical and machine learning capabilities.

3. Knowledge graph

As the amount of data still grows rapidly, it becomes increasingly difficult to analyze it. Knowledge graph can help in this by closing the gap between human and machine. In the minds of Gartner, it is one of the main data trends.

Knowledge graph has a form of a facts set (description of objects, conceptions and events) connected by typed links. It becomes possible to create a better context for data through linking and semantic metadata. It promotes easy analysis, integration, sharing and data aggregation.

4. Augmented analytics

We generate about 2500 petabytes of data every day and in 5 years this number will grow to 463 exabytes. Data increasing created serious problems in its processing. Augmented analytics can help to solve them. Using ML and AI methods big data transforms into massively smaller and analyzable one. According to the Gartner research augmented analytics will become a driver of BI in 2021.

5. Data Visualization

Data visualization became a perfect assistant in 2020 that helped to understand current situation easier. Creating, critical understanding and evaluation of data visualization will become a fundamental skill for everyone.

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