What are the Challenges with big data, and how does this blog seek to address them?
Big Data challenges include managing an enormous amount of data, which entails storing and analyzing the massive amount of data across many data warehouses. While dealing with it, numerous significant obstacles must be overcome, which require agility.
Before trying to understand the challenges of Big Data, it is necessary to understand the characteristics of Big Data. Big data is a collection of information from numerous sources, and it is frequently defined by five factors: volume, value, diversity, velocity, variability and veracity.
VOLUME: Volume the sizes and volumes of big data that businesses can handle and analyse
VALUE: Value is the most crucial “V” from a business standpoint. Big data typically has value in the insights and pattern recognition that result in more efficient operations, robust customer relations, and other tangible and quantifiable corporate advantages.
VARIETY: Unstructured data, semi-structured data, and raw data are examples of various data kinds.
VELOCITY: Velocity is the rate at which businesses acquire, store, and handle data. For example, the quantity of social media postings or search queries a company receives at specific intervals.
VERACITY: Executive-level confidence is frequently determined by the veracity or “truth” or accuracy of data and information resources.
VARIABILITY: The ever-evolving characteristics of the data businesses try to collect, manage, and evaluate—for instance, changes to the meaning of important words or phrases in sentiment text analysis.
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Challenges with Big Data
- Lack of Big Data Professionals
Data scientists, engineers, and analysts are competent to operate Big Data solutions. They are equipped to deal with the issues presented by Big Data and produce insightful solutions for the business where they are employed.
The difficulty arises from the strong demand yet scarcity of such skills.
The shortage of data professionals is sometimes caused by the fact that while data handling tools have advanced quickly, most professionals still need to.
Big Data salaries have grown significantly over time as well. Although businesses are investing in hiring professionals with these talents, there is still a gap between the demand for and supply of such competent data handlers.
Solution: Businesses are spending more on the hiring of qualified professionals. To help current employees be more efficient, training programmes must also be offered to them.
Data professionals need help to keep up with the rapid development of data management solutions. As a result, businesses spend money on data analytics tools that use Artificial Intelligence or Machine Learning. Because of this, even those without specialised knowledge may operate tools efficiently. Companies can save hiring expenses and accomplish Big Data objectives in this way.
- Data growth
Properly storing all these enormous volumes of data is one of the biggest problems with big data. Companies’ data centres and databases are storing an ever-growing amount of data. It becomes increasingly challenging to manage big data sets as they increase rapidly over time.
Most of the material is unstructured and comes from many sources, including text files, movies, audio, and documents implying that databases will not contain them. This can provide significant Big Data analytics issues that must be overcome to maintain the company’s progress.
Solution: Companies are choosing contemporary methods like compression, tiering, and deduplication to handle these massive data collections. Using compression, data can be made smaller overall by reducing the number of bits present. The elimination of redundant and undesired material from a data set is known as deduplication.
Companies can store data in various storage tiers thanks to data tiering. It guarantees that the data is kept in the best possible storage location. Depending on the size and relevance of the data, the data tiers may be flash storage, public cloud, or private cloud.
Additionally, businesses choose big data tools like Hadoop, Spark, NoSQL, and other technologies.
- Data security
One of the most significant problems of Big Data is keeping these enormous collections of data secure, particularly for companies that have access to a lot of sensitive company data or customer data. Unsecured information might serve as a haven for dangerous hackers.
Only a few businesses invest in extra data security measures specific to big data, like identification and access control, data encryption, data segregation, etc.
Data theft can cost a company millions of dollars.
Solution: To protect their data, companies are hiring more cybersecurity experts. Additional measures made to secure it include:
- Create a Device Security Policy.
- Increasing the number of cybersecurity experts
- Sensitive data encryption and data segregation
- Make use of Big Data security solutions like IBM Guardian
- Identification and access authorisation management
- Real-time security surveillance
- Data sources
Finding the right information source from the many different sources that produce data that aligns with a company’s aims or objectives is complex. As a result, merging data from many sources to provide relevant reports presents challenges for big data integration.
A company’s data can be gathered from a wide range of sources, including ERP programmes, customer logs, social media pages, financial reports, emails, presentations, and reports prepared by staff. Putting all of this data together to create reports is a complex undertaking.
It is a subject that businesses frequently ignore. However, flawless data integration is required for analysis, reporting, and business intelligence.
Solution: Companies must get the appropriate technologies to address their issues with data integration. The following list includes some of the simple data integration tools:
- Microsoft SQL
- QlikView
- IBM InfoSphere
- ArcESB
- Xplenty
- Informatica PowerCenter
- Data quality
When data quality issues contaminate big data systems, analytics algorithms and artificial intelligence applications based on big data may produce subpar results. As data management and analytics teams work to ingest more varied data types, these issues may become more severe and challenging to audit.
An organisation can obtain similar sets of data from several sources. A procedure known as data governance involves getting the data to agree with one another and keeping an eye out for accuracy, usability, and security.
One of the main concerns is managing data quality effectively. Using big data, a business may offer a highly tailored experience, identify up selling opportunities, and keep track of emerging trends. The management of data quality effectively took a lot of work.
Solution: Managing Big Data management issues and data governance might be complex with policy changes and technological advancements. To assure data accuracy, special teams are tasked with handling data governance and investing in ad-hoc data management systems.
Data scientists develop an intelligent data identifier that recognizes duplicates with little data variations and flags any potential mistakes to assure the accuracy of the data they collect. As a result, the business insights produced by data analysis are now more accurate.
Few other challenges with big data include:
- Real time or Business Insights
- Confusion in selecting tools for big data
- Data validation
- Lack of understanding big data
- Keeping costs down while scaling data systems
- High salaries of big data scientists
Real implementation difficulties are what makes big data challenging. These need to be handled immediately and with urgency because if they are not, the technology could fail, which might have unfavourable consequences.
To conclude, we may encounter various big data difficulties when we develop our data strategy. We need to consider how you gather, store, manage, use, and remove data to keep it current and ensure that anyone who needs it can still access it.
