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Major Consequences Of Having Poor Data Quality For Your Business

how data powers business opportunities

 

Organizations are collecting and generating more information than ever before but simply having a lot of data does not make a business data driven. Issues related to data quality maintenance is infecting numerous businesses. IT department which aren’t taking any steps in order to improve accuracy of their data, can lead companies to pay a big price. Generating trusted information isn’t always easy, though.  Nearly half of organizations are already in error due to poor data quality.

 

Poor data quality can impact organizations in a very negative way by have serious financial consequences. Regulatory fines, monetary losses from bad business decisions, and legal fees resulting from errors can add up to millions of dollars. IBM estimates the total cost, to U.S. organizations only, to be $3.1 trillion dollars annually. Moreover, when it comes to patient or consumer safety, bad data can cost lives.

 

A qualitative database with complete market information is very useful for the effective generation of new leads and the restructuring of existing one. Results of a campaign must be reflected in the database and information must always be accurate, complete, correct and unique. Yet this is not always the case. During customer contact, organizations too often receive answers such as: “I do not have permission to leak confidential information”, “Cloud applications? No, we do not use ‘and’ I’m not the right person for this conversation ‘. 46% of the organizations sometimes go wrong due to poor data quality, according to research by the New York Times. What price do organizations actually pay for this? I have listed the three most important consequences.

 

  1. Target-less costs are incurred

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A definite price tag is not linked to bad data quality, but that organizations make costs and miss out on profit is beyond doubt. U.S. organizations think approximately 32% of their data is inaccurate and believe this negatively affects their revenue in terms of weak resources, lost productivity, or wasted marketing and communications spend. Moving companies, changing e-mail addresses and reorganizing organizations. This means that mail is sent to incorrect addresses, e-mails do not arrive, and departments can no longer be reached. The mail is packed, the e-mail is typed, and the phone picked up, but these actions do not yield any results. Wasted time. And time is money.

 

  1. Sales and marketing without result

If companies work with outdated data, chances are that they do not have insight into who they should approach at which company. People change jobs, retire or come to the streets after a merger or takeover. If the database is not continuously updated and cleaned up with this information, effective customer approach becomes difficult. The right DMU is not available and companies do not get the right person. They do not go any further and even go two steps backwards. The target group is not reached and at the same time they strike a flatter with the potential customer. All this because companies do not have their data up to date.

 

  1. Reputation damage

As an organization, you want to avoid blunders and with a large arc for possible errors. You do not want to write to companies that are just bankrupt or are seeking contact with people who have already left the company. These missteps make sure that people talks negatively about your organization and that is the last thing you want. In short, get your facts straight. Make sure you do not run towards a wrong direction and avoid the above missteps. Provide a database containing all customer data and refresh them regularly. Only then can companies effectively carry out marketing and sales activities.

Value Creation with #BigData and #ConnectedObjects

The Internet of Things and the Big Data have extended the digital revolution to all parts of the economy. With the Internet of objects (IoT) and gathered data we are at the dawn of a new digital revolution. If #BigData helps companies to understand the behavior and expectations of their customers, the connected objects are contributing to the process.

 

Three aspects of the digital revolution in particular are shaking up technology, industry and the economy with profound social consequences: “the decrease of computing and telecommunication costs, which are gradually becoming cheap resources and easily accessible to everyone, IOT evolutions leading into an era of continuous and never-ended innovation and the desire to create something outside the box, a new economic mechanisms which in particular enables the development of activities with increasing returns that redefine the competitive rules of the game”.

IOT

 

One by one, all economic sectors are switching to the digital age by threatening disappearance of businesses that won’t evolve. Companies must consider their positioning in this new paradigm, rethink their business model, to develop new competitive advantages – those of the previous era becoming partially obsolete – and then to transform to implement the new vision.

 

Positioning and competitive advantages: Companies must first understand the potential value creation of connected objects and Big Data in their markets. Here are four key capabilities of connected objects combined with Big Data:

 

  • Monitoring: The sensors placed on the connected objects will provide with more information and control in order to identify and fix these problems. The data can also be used indirectly to better contemplate the design of future objects, to better segment the market and prices, or to provide a more efficient after-sales service;
  • Control: use of the gathered data by algorithms placed in the product or in the cloud makes it possible to remotely control the objects if they are equipped with actuators;
  • Optimization: the analysis of the current and past operating data of an object, crossed with all the other environmental data and the possibility of controlling them, makes it possible to optimize the efficiency of the object;
  • Autonomy: the combination of all previous capabilities and the latest developments in artificial intelligence allows to achieve a high level of autonomy of individual objects (such as household vacuum robots) or complete systems (such as smartgrid).

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In addition, connected objects require companies to re-evaluate their environment, as the data produced and the services and platforms that accompany them allow for system optimization on a large scale. For example, public transport is already being considered in the context of a wider mobility market, in which the aim is no longer to operate a bus or subway network, but to help a Customer to go from point A to point B.

The ecosystem then expands to include transportation facilities in and around the city (bus, metros, individual car, taxis, car-sharing, etc.). .), GPS and mobile applications, social networks of users and infrastructures of the city (road, car parks, etc.).

CONNECTED OBJECTS

Transformation of the business model: Once measured the appearance of connected objects and their impact on a defined market, companies must think of their transformation to excel in this new paradigm. First, the company must evolve most of its functions and their expertise, in terms of:

 

  • Design: connected objects are more scalable, more efficient and less energy-consuming. Greater collaboration is needed between software teams and hardware teams to design new products and services that integrate more intelligence, sensors and remote capabilities in the cloud using Big Data;
  • Marketing: the new data created by the connected objects make it possible to better segment the market and individualize the customer relationship. This individualized marketing also makes it possible to design services more easily adaptable while preserving economies of scale;
  • Customer services: the role of customer services is gradually evolving towards the prevention of breakdowns, sometimes at a distance. The analysis of the data also allows these services to understand the causes of breakdown, in particular to improve the design.

 

We are witnessing a new era of the Internet of Things that, along with Big Data and cloud computing, is one of the key foundations for companies of the future. To do their best, companies will have to acquire much more robust technological infrastructure as these objects should be created within a safe environment where we trust digital technology. More fundamentally, companies need to evolve their structure and governance to gain agility and adaptability.

Challenges of #BigData

Behind the name of #BigData is hidden an astronomical amount of data produced anywhere, everywhere at any moment by men and machines to each action they perform together and separately.

This production is exploding because 90% of the available data was created only in the last two years. Big Data today is being analyzed to discover the insights that lead to better decisions and strategic business moves.

 

Big data apps are being used to improve offers, service levels and customer support and many more. The following numbers will certainly show you the economic potential of well-established data: Only 17% of companies haven’t plan at all to launch a Big Data project but over 70% of companies have already made use of Big Data, either by integrating their business or as part of a pilot project process. The Data technologies are maturing to a point in which more and more organizations are prepared to pilot and adopt big data as a core component of the information management and analytics infrastructure. It’s an area of research that is booming but still faces many challenges in leveraging the value that data have to offer.

 

Here are so called “big challenges” of Big Data.

 

Find a language for Big Data:

All sciences, chemistry and mathematics have experienced a tremendous boost by adopting a specific language. Don’t you think we must follow the same path in the area of Big Data and invent an algebraic notation and an adapted programming language to better share and facilitate its analysis?

 

Work on reliable data: 

With the explosion in the volume of available data, the challenge is how to separate the “signal” of “data” and “valuable information”. Unfortunately at this point, a lot of companies have difficulty to identify the right data and determine how to best use it. The fight against “spam data” and data quality is a crucial problem. Companies must think outside of the box and look for revenue models that are very different from the traditional business.

 

Data access: 

Data access and connectivity can be an obstacle. McKinsey survey shows that still a lot of data points aren‘t yet connected today, and companies often do not have the right platforms to manage the data across the enterprise.

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Embedding increasingly complex data: 

If the Big Data was first concerned the “simple” data (tables of numbers, graphs …), the processed data is now more and more complex and varied: images, videos, representations of the physical world and the living world. It is therefore necessary to rethink and reinvent the big data tools and architectures to capture, store and analyze this data diversity.

 

Better integrate time variable: 

The time dimension is also an important challenge for the development of Big Data, both to analyze causalities in the long term than to treat accurate information in real time in a large data flow. Finally, the problem also arises in terms of storage. The volume of created data will exceed the storage capacities and will require careful selection.

 

IT architecture: 

 The technology landscape in the data world is changing extremely fast. Delivering valuable data means collaboration with a strong and innovative technology partner that can help create the right IT architecture that can adapt to changes in the landscape in an efficient manner.

 

Security: 

Last but not the least, we’ve security issue. Keeping such vast lake of date secure is a big challenge itself. But if companies limit data access based on a user’s need, make user authentication for every team and team member accessing the data and make a proper use of encryption on data, we can avoid a lot of problems.

 

The change of scale offered by the technologies of Big Data have generated profound paradigm shifts in scientific, economic and political fields. But it also impacts the human field.

 

Xorlogics cognitive abilities are indeed developed to treat and represent all number of data. Big Data thus puts us to the test to challenge our analytical capabilities and our perception of the world. As we change and grow, the beliefs that are most vital to us is to put the people first, follow excellence, embrace the change and act with integrity to serve the world.We at Xorlogics have exceptional expertise in the domain of Big Data like Hadoop EcoSystem (HDFS), MapReduce, Pig, Spark, Storm HBase, Cassandra, MongoDB, Hive, Sqoop, Thrift, Zookeeper, HUE, Nutch Tika, Kafka.

 

So if you are looking for more information or to gain a better understanding of big data terms, tools and methodologies don’t hesitate to contact our experts in the data field!

Key strategies of a Big Data project implementation

Focusing on Big Data and analytics to create a real competitive advantage requires a structured and effective approach to collect, clean, correlate and analyze all of the gathered data. To facilitate the task, we’ve gathered three key strategies of this process to ensure the success of your Big Data project implementation.

 

Identify and collect important data for your business: 

 

Above everything in Big Data project, it’s essential to target information to be collected in order to ensure a significant return on investment. Avoid investing in information process which cost can be higher than their potential value.

While benefits are being experienced and are expected from Big Data projects, there are complexities that organizations need to consider. The amount of data organizations have access to is expected to continue to increase. The more data there is in different forms (both structured and unstructured); the more complex processes involved to ensure that analysis of the data is comprehensive, meaningful, and useful.

An innovative approach exists and allows, from a graphical interface, easy to use, integrate data of any kind, transform and manage quality, natively in Hadoop clusters. By overcoming the complexities of programming under Hadoop, IT can respond more quickly and less costly to business demands.

 

Manage data quality: 

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Cleaning data while using or storing it in huge repositories is hardly effective. It is best to complete this step “live”: when transactions are in progress within the systems, for example when a user clicks “OK” on your web site, or an RSS feed notifies you a new message on a blog.

Correlate information from different sources is also essential. For example, you can greatly optimize your marketing actions knowing that “tcook 1968” which visited your website is the same person as “Thomas Cook” that made a credit card payment by phone last month, and visited your physical store by presenting himself as “Thomas Cook”.

Create a data quality Firewall can help you ensure the quality of data, before the gathered data spread to other departments of your company. This firewall will improve business processes, reporting and will optimize the correlation and management analysis of Big Data.

 

Analyze and disseminate information: 

 

Business users are now seeking to free themselves from complex solutions, often imposed by the IT department and poorly adapted to their daily needs. The self-service analysis tools today allow many employees to explore their data, to produce content with high added value, and share it across the organization or beyond it.

This agility, access and manipulation of data, however, must not go against the company’s governance rules. For this reason, it is essential to have a platform with security and a unique repository to handle all of the valuable data chain.

The self-service analysis, through a portal or an application, should not overcome information delivery capabilities to a large number of recipients, regardless of the medium (smartphone, tablet) a schedule or updated in real time.

 

Organizations that do not create Big Data projects in an organized, future-looking way may find that their solutions become more challenging and less appropriate to use over time.

Big Data, Big Problem?

In order to measure the progress of companies in the exploitation of their customer data, EY surveyed some known European companies. The purpose was to see the difference between the “buzz” generated by the fuzzy concept of big data and the reality of large companies. The results of this survey, conducted among more than 150 European companies, reveal that despite a largely positive perception, the “Big Data” hasn’t really taken place in reality unlike it has to be the case.
Let’s check the list below to know more about these issues that’s coming in the way of companies in order to integrate big data in their strategy.

 

Data collection via traditional channels:

Every businesses and organizations accumulate various type of data, such as financial information related to revenues and expenses, data about their customers, vendors and also about their employees. We’ve noticed that traditional file systems are still used by companies to gather data in order to increase their knowledge, understanding of customers, products and deploy marketing strategies. (Storing data in paper files, folders is a form of traditional system).

 

Unstructured data:

Companies collects huge volume of data and need valuable knowledge extracted from these data to improve their business results, still the survey reveals that 45% of respondents agrees that the data collected isn’t sufficiently exploited and only 27% of companies are equipped to manage and analyze the gathered data from many sources.

 

Analytical skills:

One of the top adoption challenges of big data is obtaining the skills and capabilities to interpret it. EY survey shows that for 70% of respondents, a team of less the 10 employees is dedicated to analyze the gathered data. Only 6% of companies have a staff of more than 50 dedicated people to decode useful information from the data.

 

A lack of data processing tools:

Good thing is that most of these large companies are aware of the progress of these unstructured data. 59% of respondents claim to anticipate an increase in the volume of data reliability within 18 months. At the same time, only 27% of them affirm that they have established internal processes to operate reliable or unstructured data.

 

Analysis of the data still (too) little predictive and real-time oriented:

Only 10% of companies operate their customer data for predictive purposes and 5% of them do it to optimize the technical process or to increase timeliness and increased storage capacities (key elements to exploit growing volumes and ever faster data and information flows).

 

The lack of mainstreaming in project management (Big) data:

39% of the respondents recognize that internal silos remain a drag on the optimal use of customer data. Each business has a habit of using and transforming data from its databases to meet its own goals or business issues, capital data cannot flow in the company, which explains the lack of a unified vision.

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The absence of ROI of Big Data projects:

Only 29% of respondents consider that the Big data is a major milestone and has the potential of big impact on business. As for the establishment of a “Big data Action Plan with concrete actions,” they are only 18% who actually did. Regarding the return on investment in particular, 58% of companies surveyed did not seek to quantify the contribution of solutions to their business performance. Again, the gap is huge between the most mature (77%) and “not mature” (3%).

 

Lack of sponsorship from the managing director:

The lack of ROI measurement, coupled with unfavorable economic conditions explain the caution of most CEO on the subject. The majority of small-medium size companies considered perception of top management as a brake on the optimal use of data within their business, 57% of them, against only 11% for the big size companies.

 

The reluctance to share personal data:

The issue related to data security, in which we can add the protection of privacy is the key to the future of the big data. The study of EY highlights that 70% of consumers are reluctant to share their personal data with companies and 49% say they are less likely to do so in the next five years.

 

Low awareness of safety issues and protection of data:

Among the companies EY surveyed, 30% believe they are not concerned with the protection of privacy issues during the operation of their customer data. 92.3% Companies, identified as the most mature in the Index EY Maturity Data, consider that the issue of protection of privacy is a priority. While for 58.6% of those who have been identified as less mature, don’t care about protection issue.

 

To resume:

Two-thirds of European companies (63%) consider that the big data is a valuable and interesting concept but still too vague, difficult to integrate within companies, in terms of organizational transformation, ROI strategy, management and training skills.

 

Even though big data is the petrol of this century, currently, half of the companies did not even studied any opportunities related to big data. Only 9% of companies surveyed have launched both the big data opportunity to study and put in place a comprehensive strategy to manage their customer data. Half of the respondents acknowledged that the absence of “a clear plan of action that constitutes a road map for the entire company” is an impediment to the optimal use of customer data. 57% of companies consider perception of top management as a brake against 11%.

 

The big data approach can be useful and beneficial for every businesses, but without a solid plan aligned with your business objectives you may miss out an elegant solution with a guaranteed return on investment.

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