Collection and management of big data is about having a mechanism to handle high variety and large volume of data, as well as handling data at high velocity. Organizations are already taking advantage of data from variety of sources, including social sites, third party systems, global partners, Web tools such as Google Analytics, ERP systems such as the Quickbooks and Microsoft Dynamics GP, CRMs such as the Salesforce CRM, Microsoft CRMs, and organization’s internal systems. Therefore, the issue of integration in Big Data is not questionable, because at the end of the day, this data must be managed centrally in a data warehouse.
Data integration is not even the main issue in Big Data phenomena today: companies must ensure that parallel processing and extreme performance are the resultant of integration efforts, because Big Data must deliver the necessary benefits to businesses. Indeed, an IBM conference in Big Data this year identified that Big Data provides competitive advantages to firms, but firms must overcome a few challenges to achieve these benefits. One sure thing is that these challenges are hard to crack.
Integrators will best be suited to advice on the Big Data phenomenon in order to maximize on the present and future Big Data integration benefits, while overcoming the existing challenges in its implementation. Here are the reasons:
1. Need to handle increasingly diverse data types versus flexibility of Big Data integration technology
In order to allow changes in data types, traditional data models designed for those databases that rarely change must go through a lengthy process. ERP systems such as the Quickbooks and Microsoft Dynamics GP, Web tools such as Google Analytics, as well as Microsoft CRMs, Salesforce CRM and others generate different data types.
Although there has been a call to have all data types be registered for data quality, the rate at which new data types are being born would basically create a delay in availing necessary data for analysis by firms. This means that the data, so delayed, would not be helpful in real time. How organizations deal with this problem is critical as they must ensure to improve quality of data and data capabilities at the same time.
This is solely a problem with Big Data integration because, once an organization begins to implement data integration in Bid Data, it discovers that there are many separate tools used for new data discovery, as well as for management and integration of global metadata, once new data types immerge: these separate tools must be integrated together.
There is no doubt that an organization could benefit from an integrator’s advise to make the most out of it: to make decisions regarding choosing data updaters or not, lowering cost of integration, and what best tools to go for in order to ensure factors such as speed of system and diverse analysis are considered.
2. Difficulties involved in identifying diverse organization’s information needs and selecting the best relevant solution from diverse Data integration technologies from vendors
It is not a question of whether an organization, big or small, needs big data integration technology or solution, but what solution they must go for to serve their needs. It is not even easy to choose from the many options available, without any criteria, even if the purpose is to expand the existing Big Data integration system in your firm. The problem is; there are so many areas and factors to capture, in order to come up with a Big Data integration strategy, which is paramount to achieving the competitive benefits.
To gain maximum benefits from Big Data integration, integrators can help a firm identify and include its diverse needs (whether technology/functional-based, customer-based, worker-based or goal-based needs) in doing a needs analysis. An integrator who is experienced with handling these technologies is best suited to advise what an organization should do and how to go about it. For instance, requirement analysis is identified as a worthy route to avoid unjustified costs and useless systems that do not give competitive advantages or solve firm’s problems.
Firms need to carry out an integration requirement analysis before sourcing for an integration solution. It is critical to consider what types of information a firm uses and produces when carrying out such an analysis. This depends on the type of business processes (construction, inventory handling, and transportation, among others). Other factors to consider in doing an integration requirement analysis include user requirements, organizational characteristics and others. It is worthy considering that different vendors avail systems that are suitable fro different needs and situations.
3. Increasing need for data security
Protection of Big Data increases with increase in data volume and data integration. It becomes more complex when data is coming from variety of sources and locations. Unfortunately, security issues are no optional when it comes to application of Big Data in marketing, because companies are dealing with personal and sensitive information. No matter what type of data and information your system handles, it is critical to safeguard data from possible losses, hijacking (if it is sensitive information) and interference.
Although organizations are required by authorities to ensure some data security level, it is imperative to identify Big Data security as a form of risk management for your firm, because of dangers involved in loosing data, having it hijacked or interfered with. Big Data security violations can increase operational and compliance costs, end up with reduced efficiencies and compromise value and quality of Big Data. How organizations must go about it in the face of Big Data integration can be better told by an integrator. What you can be sure is that traditional security technologies that cannot handle distributed and large-scale environments are less useful.
4. Challenges in re-skilling IT staff and overcoming IT barriers
No doubt it was identified this year as one of the 4 most critical challenges that must be overcome, if a firm must overcome in order to benefit from Big Data. What are the best re-skilling requirements in order to make sure Big Data works for an organization? An integrator would be best suitable to advice on the same because they understand the most common IT challenges in relation to data and system integration, which would be overcome easily by having capable IT specialists. For instance, traditional data analytics must be suitable for Big Data, and more important, Big Data analytics must focus on functions that drive business.
A firm must identify the priorities when re-skilling becomes critical, whether these priorities touch on use of best and diverse emerging integration technologies, firm’s business goals versus customer needs, or cost-related issues. For instance, different technologies such as Web technology, GPS tracking, and Smartphone technologies, among others, are being incorporated as sources of Big Data by data integration solutions. Of course, the most relevant skills will see a firm transform big data analytics to users for firm’s benefit.
5. Disagreement between Cost issue and Opportunity issues
Recently, every organization was taken by the desire to cut down costs as a way of increasing competitiveness. Many organizations ended up cutting down their expenses on technical systems and having an attitude that Big Data technologies are there to increase costs. However, research has now identified that Big Data technologies have benefits when implemented. This does not mean that companies should leave out their desire to reduce costs: rather they should make a business case by chasing opportunities brought about by Big Data technologies.
Through the help of an Integrator who understands where they can focus in order to get the benefits, for instance, they should end up focusing on customer interactions/engagements and innovations with their Big Data technologies. Should organizations focus on the FUD (Fear, Uncertainty, and Doubt) where they aim to avoid the risks involved by being blindsided about technology? Or should they go for the opportunity-approach? The former, for instance, ignores the cost analysis altogether while the latter includes it. To avoid large costs, one may look at one solution such as the our DBSync system that allows them to pull information from as many sources as possible with many integration capabilities.