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A Data Analytics Roadmap

Components of The Data Analytics

The volume of data that governments, businesses and people around the world produce is growing exponentially, animated by digital technologies. Organizations are changing their business models, building new expertise and devising new ways of managing and unlocking the value of their data.

 

Businesses around the world have recognized that data is a hugely important part of their organization. While every organization is at a different stage of “data travel,” whether it’s cutting costs or pursuing more ambitious goals, such as improving the customer experience, there is no way back. In fact, many companies are currently in the phase where data defines and drives corporate strategy.

 

Infosys recently completed a study of more than 1,000 companies with sales exceeding $ 1 billion in 12 different industries covering the US, Europe, Australia and New Zealand regions. The aim of the survey was to obtain a comprehensive overview of the data travel undertaken by the surveyed organizations and to see how they are analyzing their data to achieve more succes.

 

The study found that more than 85% of surveyed companies have an enterprise-wide data analytics strategy. This high percentage is not surprising. However, having a strategy is not everything: there are more aspects that organizations need to consider to successfully exploit the potential of their data. First of all, companies need a defined strategy that covers several areas. Second, according to the strategy, execution must be seamless, and that is the challenge.

 

Developing a sound strategy is the foundation. However, in terms of data, it is no longer about identifying metrics and KPIs, developing management or operational reports, or improving technology. Rather, it should cover all areas of the company. In short, the data strategy is now an important part of the business – this is heralding a shift away from traditional approaches.

The survey also highlighted that surveyed enterprises across different industries are meeting challenges that blocking them from implementing a right data analytics strategy. 44% of them stated integrating multiple datasets across sources as their biggest challenge and 43% are facing the biggest challenge to understand the right analytics technique to be deployed.

 

data analytics

 
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What are the characteristics of a good data strategy?

To begin with, it must be ensured that the data strategy aligns with the overall corporate strategy and is closely aligned with the business objective, it can be increasing growth or profitability, managing risks, or transforming existing business models. In addition, a flexible data strategy is important so that regular reviews and updates can keep up with changes in the business and marketplace and drive innovation – faster, better, and more scalable.

Organizations need to create data strategies that match today’s realities. To build such a comprehensive data strategy, they need to fulfill current business and technology commitments while also addressing new goals and objectives. A good data strategy must be bidirectional to track current business trends and to provide helpful insights for the future. This approach is only possible if companies pursue a multi-level data strategy that includes people, technology, governance, security and compliance, and finally a suitable operational model.

The data strategy must define a value framework and have a revenue tracking mechanism to justify the investments made. About 50% of the study participants agreed that a clear, pre-determined strategy is essential for smooth execution.

 

The best strategy is useless if the execution falter

There are some hurdles that can stop proper implementation of a data strategy. Technology-related challenges can already arise in choosing the right analysis tools, the availability of people with the skills they need, next gen capabilities, and so on. Most of the challenges identified in the study occurred during the execution phase. Although they seem enormous at first glance, they can be addressed with a careful planning. Preparing for multiple regions, locations, suppliers, and talent acquisition and training are a few ways to pave the way for smooth execution.

 

What role do external technology providers play in this?

An experienced external technology vendor can contribute at multiple levels, from helping define business and data strategies that work together, identifying loopholes in existing business models, transforming business and technology solutions to developing, implementing, and maintaining of best-in-class technology solutions.

 

In a world based on data, businesses need to do everything they can to adapt to a customer-centric strategy. Partnering with a high-performance technology provider can help companies better meet their business goals.

 

How Companies Can Leverage Their Existing Data Assets To Unlock New Business Opportunities?

Have you already heard about the latest update about Facebook who wants to play Cupid? At their F8 developer conference, the social network announced its entry into the online dating service. Why not? As Facebook users were able to reveal their relationship status since February 2004, with the existing user data form the ideal source it’s possible to find the perfect partner with the help of a suitable algorithm. However, this operation requires valid and high-quality data. At the same time, this announcement is a very good example of how companies can leverage their existing data assets to unlock new business opportunities.

 

Business can generally succeed in improving their own data quality by improving their data governance processes and the developing suitable strategies for complete data management. First of all, it is important to define the criteria for good data, which may vary depending on the company’s activity. These include aspects such as relevance, accuracy and consistency – in this case, data from different sources should not contradict each other. It’s also helpful to investigate where errors in master data are particularly likely to creep. Because here too the well-known programming wisdom applies: garbage in, garbage out. Poor data sources lead to poor results.

 

In practice, sources of error can be found throughout the value chain of data management. These can be human input errors during data acquisition, defective sensor data or incomplete data imports in automated processes. But also, different formats of data can lead to errors, in the simplest case when one has to input the data in US, in case of uncertainty about whether the metric or imperial (Anglo-American measurement system) is used. In addition, organizational deficiencies lead to data errors, for example, if it is not clearly defined who is responsible for which data sets.

 

In order to achieve more quality data, five points can be identified that help to increase the value of your own data.

 

Clarify goals:

 

Everyone involved in the project should agree on the business goals to be achieved with an initiative for better data quality. From sales to marketing to management, each organizational unit has different expectations. While decision-makers need more in-depth analysis with relevant and up-to-date information, it may be critical for a sales representative that address data is accurate and complete.

 

Find and catalog data:

 

In many organizations, data is available in a variety of formats, from paper files and spreadsheets to address databases to enterprise-class business applications. An important task is to localize these databases and to catalog the information available there. Only when the company knows what data can be found in which database and in what format, a process for improving the data quality can be planned.

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Harmonization of data:

 

Based on the initial inventory, a comparison is now made with the target to be achieved. This can result in a variety of tasks, such as a standardization of spellings, data formats and data structures. It uses tools for data preparation and deduplication to provide a harmonized set of data, while data profiling solutions help analyze and evaluate data quality.

 

Analysis, evaluation and processing:

 

If you consolidate your data and process it in a cloud, data lake or data warehouse, you can flexibly perform a wide variety of data preparation tasks there by using data integration and data management software solutions. Anyone who has to process streaming data, originated from sensors or Internet of Things, has the option of using cloud resources to check the incoming data in a very flexible way and to sort out fake data packets.

 

Establish continuous processes:

 

Ensuring data quality is a continuous process. After all, new data is always collected and integrated into our own systems. Even if external data sources already provide high-quality data for further processing, it is still necessary to constantly check and validate your own data stocks via a data monitoring system. There are very different solutions, such as, self-service data cleansing solutions, rule-based data transformation applications, self-learning software solutions that independently monitor data formats and detect and correct statistical anomalies. Already today, algorithms for deep learning or artificial intelligence are able to handle many tasks around data management in big data scenarios. However, it is important that responsibilities for data management are named and that quality assurance processes are confidently secured in the company’s processes.

 

Conclusion:

 

Managing quality data is a team work that spans in all functional areas of a company. Therefore, it makes sense to provide the employees in the departments with tools to secure the data quality in self-service. In particular, cloud-based tools that can be rolled out quickly and easily in the departments are ideal for this. Thus, equipped companies can succeed gradually in improving their data quality and increasing the value of their data. This leads to satisfied employees and happy customers.

Data Analytics Trends for 2018

Using data profitably and creating added value is a key factor for companies in 2018. The world is becoming increasingly networked and ever larger amounts of data are accumulating. BI and analytics solutions and the right strategies can be used to generate real competitive advantage. Here below are listed the top tends concerning Data Analytics of 2018.

 

How new technologies support analysis

Learning (ML) technology is getting improved day by day and becoming the ultimate tool in creating in-depth analysis and accurate predictions. ML is part of the AI that uses algorithms to derive modules from structured and unstructured data. The technology supports the analysts with automation and thus increases their efficiency. The data analyst no longer has to spend time on labor-intensive tasks such as basic calculation, but can deal with the business and strategic implications of analysis to develop appropriate steps. ML and AI will therefore not replace the analyst, but make its work more efficient, effective and precise.

 

Natural Language Processing (NLP)

According to Gartner, every second analytical query on search, natural language processing (NLP) or language should be generated by 2020. NLP will allow more sophisticated questions to be asked about data and relevant answers that will lead to better insights and decisions. At the same time, research is making progress by exploring ways in which people ask questions. Results of this research will benefit data analysis – as well as results in the areas of application of NLP. Because the new technology does not make sense in every situation. Their benefit is rather to support the appropriate work processes in a natural way.

 

Crowdsourcing for modern governance

With self-service analytics, users from a wide range of areas gain valuable insights that also inspire them to adopt innovative governance models. The decisive factor here is that the data is only available to the respective authorized users. The impact of BI and analytics strategies on modern governance models will continue in the coming year: IT departments and data engineers will only provide data from trusted data sources. With the synchronized trend towards self-service analytics, more and more end users have the freedom to explore their data without security risk.

 

More flexibility in multi-cloud environments

According to a recent Gartner study, around 70%of businesses will implement a multi-cloud strategy by 2019 in order to stop being dependent on a single legacy solution. With a multi-cloud environment, they can also quickly define which provider offers the best performance and support for a given scenario. However, the added flexibility of having a multi-cloud environment also adds to the cost of allocating workloads across vendors, as well as incorporating internal development teams into a variety of platforms. In the multi-cloud strategy, cost estimates – for deployment, internal usage, workload, and implementation – should, therefore, be listed separately for each cloud platform.

 
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Increasing importance of the Chief Data Officer

With data and analytics now playing a key role for companies, a growing gap is emerging between responsibilities for insight and data security. To close them, more and more organizations are moving to analytics at the board level. In many places, there is now a so-called Chief Data Officer (CDO) or Chief Analytics Officer (CAO), who has the task to establish a data-driven corporate culture – that is to drive the change in business processes, overcome cultural barriers and the value of analytics to communicate at all levels of the organization. Due to the results orientation of the CDO / CAO, the development of analytical strategies is increasingly becoming a top priority.

 

The IoT innovation

The so-called Location of Things, a subcategory of the Internet of Things (IoT), refers to IoT devices that can calculate and communicate their geographical position. On the basis of the collected data, the user can also take into account the location of the respective device as well as the context that may be involved in the evaluation of activities and usage patterns. In addition to tracking objects and people, the technology can also interact with mobile devices such as smartwatches, badges, or tags, enabling personalized experiences. Such data makes it easier to predict which event will occur where and with what probability.

 

The role of the data engineer is gaining importance

Data engineers make a significant contribution to companies using their data for better business decisions. No wonder that demand continues to rise: from 2013 to 2015, the number of data engineers has more than doubled. In October 2017, LinkedIn held more than 3,500 vacancies under this title. Data engineers are responsible for extracting data from the company’s foundational systems so those insights can serve as decision-making basics. The data engineer does not just have to understand what information is hidden in the data and what it does for the business. He also has to develop the technical solutions to make the data usable.

 

Analytics brings science and art together

The use of technology is getting easier. Everyone can “play” with data today without having to have deep technical knowledge. Researchers who understand the art of storytelling are pursued for data analysis. More and more companies see data analysis as a business priority. And they recognize that employees with analytical thinking and storytelling skills can gain competitive advantage. Thus, the data analysis brings together aspects of art and science. The focus shifts – from simple data delivery to data-driven stories that lead to concrete decisions.

 

Universities are intensifying data science programs

For the second time in a year, the Data Scientist ranked first in America’s annual Glassdoor ranking of the best jobs in America. The current report by PwC and the Business-Higher Education Forum shows how high applicants with data knowledge and analytical skills are in the favor of employers: 69% of the companies surveyed indicated that they would prefer suitably qualified candidates over the next four years instead of candidates without appropriate competencies. In the face of growing demand from employers, it is becoming more and more urgent to train competent data experts. In the United States, universities are expanding their data science and analytics programs or establishing new institutes for these subjects. In Germany too, some universities have begun to increase their supply.

Ten tips to avoid your #BigData project failure

If you’ve shown some interest to this week blog post, we assume that it’s because you want to avoid making mistakes that could cause your Big Data project failure.

 

Here are some points you must consider:

 

✓ Begin any Big Data project with a data review and classification process. In order to design a powerful storage architecture you must determine whether data is structured, unstructured, qualitative or quantitative. It is also a good idea to estimate the growth of data based on past trends and future strategies.

 

✓ Create a simple overview of how data flows within your organization. Having a simple diagram showing where data is created, stored, and circulated is useful when it comes to work within a group. Putting everyone on the same wavelength can help you avoid misunderstandings that are expensive.

 

✓ Consider future data storage requirements based on the success of the Big Data project. Big Data projects may reveal new information or require you to change business processes. Information from the project may in other hand require additional storage capacity, resulting in exponential growth in capacity requirements. Always think in long term.

 

✓ Be flexible. Many projects are based on both scale-up and scale-out storage technologies. Each organization and project is unique. The choice of a storage technology must be based on the objective to be achieved and not on a particular technical architecture. Many vendors offer scale-up and scale-out products that can work together.

 

✓ Data storage requirements may increase, but consider to move automatically low-visited data to a cheaper, slower storage device. Removal is also a considerable option in the long term. Regardless of where the data comes from, where it is processed and where it is stored, it always has a useful life. Deciding to delete data is a complex task, but this can enable you to realize huge savings in long term. Automatically moving data to a slower storage device is an easier option that always involves great benefits.

 
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✓ Ask providers what will happen when you reach capacity limits or theoretical performance limits. Even if you start with a small Big Data project, it will surely get bigger with time. Understanding how the chosen technology can evolve will help you avoid unpleasant surprises in the years to come.

 

✓ Be prepared for the worst. Even the simplest machines can eventually break down or black out. Ask your vendor what would happen if various elements of the storage platform were to fall. A well-designed system should never have a point of failure.

 

✓ Create a quota system early in the project to prevent future management problems. IT projects tend to occupy all available space if they are not controlled. Quotas allow you to define how much space each user or project can access. Assign the responsibility for managing this capacity to an entity responsible for this data or define a policy.

 

✓ Always involve IT security experts in your Big Data projects. Digital data has value. Even though the Big Data project involves only one research group, the IT security team must be involved from the start, so that security remains at the heart of the project.

 

✓ Don’t forget to take management time into account when it comes to calculate storage costs. Total storage costs must include the time required to supply and manage the platform. A resilient, highly automated system that does not require a full-time administrator saves far more money in the long run than a less expensive hardware that requires a lot of manual labor.

 

I really hope these tips will help you in your project planning. In case you’ve any question or need an advice please feel free to write us and our #DataHeros will contact you asap!

 

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