Big data is possibly the most significant technological development of the 21st Century. Companies are expected to spend $103 billion on big data solutions by 2027.
Although the benefits of big data technology are incredibly clear, there are also some challenges that could be constraining growth. Addressing these issues could do wonders for the demand for big data products and services.
One of the biggest challenges has been improving data cohesion. CIOs Tim McBreen and Carrie Schumaker talked about the need to centralize data and create a data fabric that spans enterprises. This is going to prove to be one of the most important changes in the growth of the big data industry.
Understanding the Nature of Big Data to Create a Foundation of Cohesion
Creating a data fabric to foster better organizational data cohesion can be very beneficial. However, it is important to first grasp the basic concept of big data.
Simply put: Big data is a mix of structure, unstructured and semi structured data that companies collect. Firms and companies can use this data to mine for information to use in predictive modelling, machine learning projects, and similar analytical applications.
Today, data management architectures in firms significantly rely on systems that utilize big data along with tools that assist in big data analytics. Experts generally characterize Big Data by three V’s, namely:
- The massive volume of data existing in various environments;
- The extensive variety of data types that are typically stored in big data systems; and
- The velocity of big data generation, collection, and processing.
Big data storage and processing
Systems often store big data in a data lake. While relational databases dictate the construction of data warehouses that store structure data only, data lakes support almost all types of data. NoSQL databases, Hadoop clusters, cloud object storage services, and other big data platforms generally define data lakes.
Those offices or companies that process big data rely on fundamental compute infrastructure. There are clustered systems responsible for workload processing distribution among commodity servers that use Hadoop or Spark processing systems. These cluster systems provide the necessary computing power.
The main challenge for organizations is how to accumulate such processing capacity through cost-effective ways. For now, that solution comes from the cloud that has formed a primary location for most big data systems. Companies can station their cloud-based systems or rely on the big-data-as-a-service that cloud providers offer.
Creating uniformity and cohesion when instituting big data analytics
Accessibility is the backbone of a big data strategy built on cohesion. Data scientists and analysts must understand the importance of accessible data and have an idea of what to look for. Armed with this information, data scientists can use analytic data systems to obtain logical and applicable results. It means data preparation is an essential early step for developing a big data analytics strategy that spans the organization.
Data preparation mainly includes:
- Cleansing
- Profiling
- Validation
- Data set transformation
As an example of customer data, some of the analytics that you can do with big data sets are:
- Comparative analysis;
Data scientists use it to examine customer behavior criteria and real-time customer engagement. The two fields help them juxtapose a business’ services, products, and branding against their competitor’s.
- Social media understanding;
Data scientists use it to analyze what people are chirping on social media about a company and their services. The step allows a company to acknowledge their weakness and find a targeted audience for its campaigns.
- Marketing analytics;
The marketing team can use information gained from this step to boost their branding campaigns and product/service promotion.
- Sentimental analysis;
Data scientists can analyze the complete customer data collected to understand how they perceive the brand, user satisfaction levels, and suspected issues.
Technologies for creating a uniform big data strategy
Organizations striving to create better data cohesion must have the right technology in place. Initially, Hadoop, an open-source distributed processing framework, was the only framework supporting big data architectures. Later, Spark development and other processing engines took the limelight off MapReduce, the engine within Hadoop. It paved the way for an ecosystem of big data technologies deployed together, but different applications can use it.
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IT vendors offer several big data platforms that combine several technologies into one package, mainly through the cloud. These platforms are:
- Amazon EMR
- Google Cloud Dataproc
- Cloudera Data Platform
- Microsoft Azure HDInsight
- HPE Ezmeral Data Fabric
Organizations can opt to establish their big data system within the premise or in the cloud. For them, the technologies available apart from Spark and Hadoop are the following tool categories:
- Storage warehouses like Hadoop Distributed File System (HDFS) and cloud object storage services like Amazon Simple Storage Service (S3), Azure Blob Storage, and Google Cloud Storage.
- Data lake and data warehouse platforms such as Delta Lake, Amazon Redshift, Google BigQuery, Snowflake, Kylin.
- SQL query engines: Drill, Impala, Trino, Presto, Hive.
- Cluster management frameworks such as Mesos, YARN (Yet Another Resource Negotiator), Kubernetes.
- Flink, Samza, Hudi, Kafka, Storm and other stream processing engines.
- NoSQL databases like CouchDB, Cassandra, MarkLogic Data Hub, Neo4j, MongoDB, Redis.
The human link to big data
Big data can reap many benefits and profits for a company, but that depends on the employees managing that data. Some big data tools allow users with a non-technical background to use predictive analytic tools for big data tasks. Big data is a valuable source that plays a vital role in future ideas like artificial intelligence, where machine learning is at the core.
Cohesion is the Key to a Successful Organizational Data Strategy
The importance of data cohesion cannot be overstated. Every organization needs to adapt a big data strategy that relies on accessibility and uniformity.
