Because of the constantly evolving data sources and the increasing amounts of generated data, companies face severe problems in achieving high-quality data integration. Those challenges altogether can also be called "The 4 V's of Big Data". They are data Veracity, Volume, Variety, and Velocity.
This term represents data inconsistency, uncertainty, and ambiguity and low-quality. It also refers to incomplete data with the presence of errors and outliers. Such data create a great challenge for the companies because they have to convert it into a consistent, consolidated, united source of information and business intelligence.
The main prerequisite for lack of data veracity and the reason why data ends up being incomplete and full of outliers are data silos. The existence of silos ensures uneven distribution of data throughout the organization's departments, as well as duplication of data-sets. This leads to inconsistency and a lack of coordination between the different data silos.
The reason for the duplication is the fact that the databases in the business entity collect, process, store, and organize data in different ways. As a result, the data format varies across silos and diverse locations, as it can be outdated, incomplete, faulty, or with insufficient quality. All these factors disable the business entities to have access to trustful, integratable data-sets which to drive business intelligence. Data silos prevent integrated data availability across the departments, and this causes difficulty in the creation of united corporate and data strategies and objectives.
Growing business entities often embrace new systems, devices, platforms, AI, or IoT networks to drive new opportunities, success, and optimized performance. On the other hand, the adoption of Digital Culture contributes to the integration of many social media and digital channels to facilitate the communication between employees, leaders, customers, and departments.
However, the adoption of so many data channels and the collection of the entire volumes of the generated data, create problems for the organizations. Overwhelming volumes make data processing and integration difficult, and at the same time, require big storages, which can generate huge costs for the companies. Storing too much data is not an opportunity, but a drawback for the data-driven companies. It makes the identification of specific data-sets challenging: just as if you look for a particular red T-shirt in a huge closet with a thousand more red T-shirts. That is why the organizations face the challenge of identifying the optimal data storage that they can handle: according to both financial and operational ability.
The collection of omniformat and omnichannel data brings many advantages to cross-industrial organizations: identify opportunities, measure progress, make evidence-based decisions, strategic plans, and effectively measure risk. Yet, these data create challenges for Digital Culture organizations.
The presence of structured, unstructured, and semi-structured data with different formats in one place is a problem, which mainly occurs when the data is either collected and stored in data lakes, or when it is siloed.
Even though lakes are inexpensive data storages, which allow depositing tons of unstructured data sets, with different formats and from different sources, they create an issue for the companies when a particular data set should be extracted and used for data integration. On the other hand, as already mentioned in the previous section, siloed data equals isolated data, which cannot be effectively used as a powerful and effective business tool.
Different data formats require different approaches when being utilized in the Big Data integration process. And to have a different approach, the focus of the business entity is being reallocated, as it should design a very complex and composite data integration architecture (which supports various formats) in order to contextualize the data, give the company a holistic view of the operations and provide valuable business intelligence.
This term represents how quickly data is being collected, stored, and processed. In other words, this is the speed of data.
Often, when the data volume, as well as its variety, are big, they have a significant impact on the speed. And slow data processing creates challenges for the data-driven companies because slow data velocity leads to operational and process delays, which, in the long-run, have a negative impact on the strategic planning of the organizations. And as the CEO of Amazon Jeff Bezos said: “speed matters in business.”
Nowadays, companies from distinct industries recognize Big data as a key asset for successful strategic decision-making. And it is an organizational responsibility to ensure the quality and speed of this asset. Otherwise, the business bears the risk of using outdated information, which is inconsistent with the organizational immediate needs and requirements.
Tackling the V`s
The challenges of the data-driven companies regarding data integration are four, but fortunately, the solution is only one: Data Governance policy.
By 2020 Data Governance will no longer be an option but will become a necessity for ensuring the right volumes of trustful, high-quality, and consistent data at the right speed.
DG allows cross-industry organizations to efficiently break down data silos and manage complex data sets, as well as to improve the navigation experience of the work units through the collected data. This will enable valuable insights to be derived, and the data will provide high-quality business intelligence to ensure successful planning, decision-making, performance, and maintaining a competitive edge. What is more, great Data Governance policy makes business entities auditable and compliant with the laws and regulations in the sector and country of operation and allows them to achieve a 360-degree view of their data.
Every business entity that has implemented digital culture or uses Big Data as the main tool for development and successful performance faces the contemporary challenge of ensuring high-quality data integration. But in order to give a solution to this challenge, companies should look into the veracity, volume, variety, and velocity of data. This will help the companies to achieve high-quality, trustful, fast and affordable data assets and will motivate them to make the best out of their data analytics.