Business leaders have been bewitched by big data’s promise of higher returns and greater customer understanding. A lot of high profile successes have seized the headlines, fast-tracking big data investments. Those who modified their business models to take advantage of the data divulge have managed to reap dividends: higher revenue and better customer engagement.
However, what is not talked enough is the fact that a plethora of big data projects hit a plateau, not standing up to the promise of a pot of gold. Lack of technology and adept resources combine to produce these sub-optimal results.
A big data project becomes successful when enterprises can capture, store, integrate, and govern data without difficulty. The degree of success depends on how well big data is integrated and analyzed for making quality-based decisions. Ergo, in the absence of a sound big data integration strategy, companies are bound to incur losses.
In this blog, we focus on some common mistakes organizations make while integrating big data and how to avoid them.
Not picking a sound data integration technology that can ingest big data
Retaining obsolete data warehousing models rather than building next-gen big data architecture patterns
Ignoring pertinent data integration principles
Undervaluing the significance of governance
Not aligning with self-service capabilities
Fixing these five essential mistakes will kick start your big data integration project.
1. Investing in a modern data integration technology with large file data ingestion capability
Big data integration projects can be successfully executed with a modern integration tool in place. However, the integration solution one picks must be able to stand up to the stresses of 4Vs of big data. Additionally, the chosen data integration platform must scale with the big data growth to adapt changes happening in the business landscape. User productivity must also be high to streamline development, enforce quality and shorten time-to-market. Modern data integration solutions can easily handle the 4Vs of big data without the need for any specialized custom code or hardware appliances. These platforms do not need IT support, thereby allowing IT to focus on more important tasks such as governance.
2. Maintaining big data architecture patterns
Big data architectures usually co-exist with traditional data warehouses. To build one using the same old principles will restrict the value of data. As a resolution, big data reservoirs can prove extremely useful as they allow data access to improve speed and performance. Modern big data architecture emphasizes streaming to facilitate real-time data ingestion and minimize risk.
3. Prioritizing data ingestion and data transformation
The right data integration technology will ingest data in real-time and automate data transformation using AI to facilitate better decision-making. The decisions made using timely insights help companies deliver delightful customer experiences and drive revenue. The right data integration tool allows user take a non-invasive approach to capture data in real-time and use it accordingly.
4. Incorporating data governance
Good governance is contingent on technology used, organization’s culture, and quality of business processes. Modern data integration technology bolsters governance by offering data transparency, accountability, and identifying areas of process and performance improvements.
5. Aligning with self-service capabilities
Modern data integration with self-service capabilities empower users to integrate large volumes of big data themselves. This self-sufficiency reduces the burden on IT as they no longer have to support users or customers for executing big data integrations. Users can easily create connections, integrate customer data, and gather customer intelligence. This drives revenue and makes organizations easier to do business with.
Use modern integration technology such as Adeptia Connect to capitalize on big data today!